MBA em Ciência de Dados¶

Universidade de São Paulo, São Carlos, Brasil¶

TCC Gabriel dos Santos Scatena 2023

Democratizando a concessão de crédito com base em dados do Open Finance¶

Democratizing credit approval based on Open Finance data¶

Área de Concentração: Ciências de Dados

Orientador: Prof. Dr. Júlio Cezar Estrella

%cd '/content/drive/My Drive/Colab Notebooks/'

/content/drive/My Drive/Colab Notebooks

!jupyter nbconvert --to html -theme classic TCC_MBA_GabrielScatena.ipynb

[NbConvertApp] Converting notebook TCC_MBA_GabrielScatena.ipynb to html
/usr/local/lib/python3.10/dist-packages/nbconvert/filters/widgetsdatatypefilter.py:71: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented.
  warn(
[NbConvertApp] Writing 30812347 bytes to TCC_MBA_GabrielScatena.html

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Conteudo¶

Criar um modelo de aprendizado de maquina para risco de crédito

Coleta de Dados
Limpeza de Dados (Data Cleaning)
Exploração e Análise de Dados
Pré-processamento de Dados
Depuração de Dados (Data Cleansing)
Lidando com Classes Desbalanceadas
Engenharia de Variáveis (ou Engenharia de Características)
Seleção de Variáveis
Seleção de Modelo
Treinamento de Modelo
Avaliação de Modelo
Ajuste de Hiperparâmetros
Interpretação de Modelo
Implantação de Modelo
Monitoramento e Manutenção de Modelo

ABSTRACT¶

SCATENA, GABRIEL DOS SANTOS. Democratizing credit granting based on Open Finance data. 2023. Monografia (MBA em Ciências de Dados) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2023.

Credit granting in Brazil presents a significant challenge, especially for self-employed workers and entrepreneurs with informal and variable incomes. Throughout history, access to credit has been historically low for this group of professionals compared to other countries. This is largely due to the fact that financial institutions traditionally use criteria such as stable income history and cash flow in credit analysis. These criteria end up excluding those with irregular or informal incomes, making it difficult for them to obtain credit for investments, business expansion, or even to cope with unforeseen financial challenges. Given this reality, it is essential to find ways to improve credit analysis and granting for these professionals in order to help them overcome financial obstacles and drive their economic development. This thesis proposes the development of a model based on Open Finance data, or Open Finance System, which can assist financial institutions in making more accurate and fair credit decisions, aiming to improve credit access and the financial situation of these individuals.

Keywords: Open Finance, Credit, Machine learning.

RESUMO¶

SCATENA, GABRIEL DOS SANTOS. Democratizando a concessão de crédito com base em dados do Open Finance. 2023. Monografia (MBA em Ciências de Dados) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2023.

A concessão de crédito no Brasil apresenta um desafio significativo, especialmente para...

Objetivos Específicos¶

Avaliação de um modelo preditivo para risco de crédito com base em dados de Open Finance.
Avaliação de vários algoritmos de aprendizado de máquina.
Comparação das métricas dos modelos obtidos com o modelo utilizado pela empresa fornecedora dos dados.
Um tutorial prático para modelagem de risco de crédito: uma Perspectiva Técnica sem Discussão de Negócios.

"Construir um modelo de aprendizado de máquina exige equilíbrio entre habilidades de negócios, científicas e técnicas... Pela minha experiência, você precisa dedicar muito tempo à parte de negócios, por exemplo, como definir um objetivo, às vezes não é fácil, como selecionar amostras para obter o melhor desempenho, isso é uma das coisas mais importantes." - Zheng

Criando um Modelo de Aprendizado de Máquina para Risco de Crédito¶

#pip install seaborn pip scikit-learn matplotlib numpy pandas plotly imbalanced-learn lightgbm xgboost catboost

# Criando um dataframe artificial para testes
import pandas as pd
import numpy as np

# Number of rows in the DataFrame
num_rows = 1900

# Number of columns in the DataFrame (excluding 'user_id' and 'label')
num_columns = 54

# Number of unique banks
num_banks = 10

nan_percentage = 0.36
zero = .95
one = .05

# Generating random data for the DataFrame
data = {
    'user_id': np.arange(1, num_rows + 1),
    'bank': np.random.randint(1, num_banks + 1, size=num_rows),
}

# Calculate the number of columns with specific values
num_zero_columns = int(num_columns * 0.08)
num_range_columns = int(num_columns * 0.50)
num_remaining_columns = num_columns - (num_zero_columns + num_range_columns)

# Adding the label X0000x columns with continuous data
for i in range(1, num_columns + 1):
    col_name = f'X{i:04}'

    if i <= num_zero_columns:  # 8% columns with only 0 values
        data[col_name] = 0
    elif i <= num_zero_columns + num_range_columns:  # First 5% columns
        data[col_name] = np.random.randint(1, 1400001, size=num_rows)
    else:  # Remaining columns
        col_values = np.random.randint(-90000, 35001, size=num_rows)
        col_mean = col_values.mean()
        col_diff = 1 - col_mean

        # Introduce NaN values mixed among existing values
        nan_indices = np.random.choice(num_rows, size=int(num_rows * nan_percentage), replace=False)
        col_values = col_values.astype(float)  # Convert to float
        col_values[nan_indices] = np.nan

        data[col_name] = col_values + col_diff

# Adding the 'label' column with values 0 or 1
data['label'] = np.random.choice([0, 1], size=num_rows, p=[zero, one])

# Creating the DataFrame
df = pd.DataFrame(data)

# Extracting 'user_id', 'bank', and 'label' columns
user_id_col = df['user_id']
bank_col = df['bank']
label_col = df['label']

# Removing 'user_id', 'bank', and 'label' columns
df = df.drop(columns=['user_id', 'bank', 'label'])

# Shuffle the remaining columns randomly
df = df.sample(frac=1, axis=1)

# Rename the shuffled columns with names X0001 to X0540
new_col_names = ['X{:04}'.format(i) for i in range(1, num_columns + 1)]
df.columns = new_col_names

# Re-insert 'user_id', 'bank', and 'label' columns at their respective positions
df.insert(0, 'user_id', user_id_col)
df.insert(1, 'bank', bank_col)
df.insert(len(df.columns), 'label', label_col)
df = df.astype(float)

# Set display options to format with no decimal places
pd.set_option('display.float_format', '{:.0f}'.format)

# Print the first few rows of the DataFrame
display(df.head())

# Print the summary statistics of the DataFrame
display(df.describe())

notebook_path = 'C:/Temp/Codes/MBA/TCC_MBA_GabrielScatena.ipynb'

# Código para medir o tempo de execução de cada célula # Code to measure the execution time of each cell
import time

def start_timer():
    global start_time
    start_time = time.time()

def end_timer():
    end_time = time.time()
    execution_time = end_time - start_time
    execution_time = round(execution_time,2)
    print(f"Tempo de execução: {execution_time} segundos")

# Chama start_timer() no início de cada célula
start_timer()


# Chama end_timer() no final de cada célula
end_timer()

Tempo de execução: 0.0 segundos

0. Imports and Directories¶

# Importar bibliotecas necessárias para o projeto
start_timer()
import pickle
import re
import os
import pandas as pd
import numpy as np
from numpy import nan
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from plotly.subplots import make_subplots
import plotly.express as px
import plotly.offline as pyo
import warnings
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import plotly.graph_objects as go
from sklearn.ensemble import IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.impute import SimpleImputer


# Configurações de exibição. Aumentar o número máximo de linhas.
pd.set_option('display.max_rows', 600)

# Supressão de Avisos :)
warnings.filterwarnings("ignore")

# Gera o caminho para o usuário local
home_directory = os.path.expanduser('~')
home_directory += '/Klavi/Klavi Directory Cloud - Data_BRAZIL'
home_directory = home_directory.replace('\\', '/')
print(f"Home directory: {home_directory}")
# Home directory: C:\Users\GabrielScatena

# Gera o caminho para o diretório de trabalho
files_directory = 'C:/Temp/Codes/MBA'
end_timer()

Home directory: C:/Users/GabrielScatena/Klavi/Klavi Directory Cloud - Data_BRAZIL
Tempo de execução: 3.65 segundos

1. Coleta de Dados¶

Coletar os dados relevantes de várias fontes ou bancos de dados.

A Klavi é uma fintech líder no setor de Open Finance no Brasil, com um lema de "revolucionar o sistema financeiro e capacitar empresas com soluções de Open Finance, possibilitando a inovação de produtos e serviços para seus clientes." Inicialmente, a Klavi forneceu um banco de dados com mais de cinquenta mil usuários registrados. Esse banco de dados foi gerado nas fases iniciais da empresa e foi derivado de dados de Open Finance, que foram preprocessados para criar as características utilizadas pela empresa. No total, existem 540 variáveis rotuladas de K00001 a K00540.

# Carrega o arquivo de dados sem rótulos
start_timer()
# Devido ao tamanho do arquivo, o mesmo foi dividido em partes para serem carregadas
file_path = rf"{home_directory}\99_Backup_Old\Temp\K360_20230404080101.csv"

chunk_size = 100000
chunks = []

for chunk in pd.read_csv(file_path, chunksize=chunk_size):
    chunks.append(chunk)

base = pd.concat(chunks, ignore_index=True)

display(base.head())
display(base.tail())

# Carrega o arquivo de dados com rótulos
labels = pd.read_excel(r"C:\Temp\Codes\y_pred_proba_banc_pan_total.xlsx")
print('\nDados rotulados')
display(labels.head())
end_timer()

Dados rotulados

Tempo de execução: 25.885830402374268 segundos

# Junção dos dados
start_timer()
new_dataset = pd.merge(base, labels, on='user_id')

duplicate_count = new_dataset['user_id'].duplicated().sum()
print('The number of rows and columns is:',new_dataset.shape ,'\nThe number of duplicated values of user_id is',duplicate_count, '.')
display(new_dataset.head())
end_timer()

The number of rows and columns is: (27188, 552) 
The number of duplicated values of user_id is 4664 .

Tempo de execução: 0.5981287956237793 segundos

O número de dados duplicados é devido ao fato que cada 'user_id' pode ter mais de uma conta bancária conectada.

2. Limpeza de Dados (Data Cleaning)¶

Remover quaisquer colunas irrelevantes ou desnecessárias que não contribuirão para o desempenho do modelo.
Verificar inconsistências, outliers e erros nos dados.
Lidar com pontos de dados ausentes ou incompletos.

# Manter somente usuarios com uma unica conta conectada para evitar ambiguidade dos rotulos
start_timer()
new_dataset_unique = new_dataset[~new_dataset['user_id'].duplicated(keep=False)]
print(' Existem',new_dataset_unique.shape[0], 'user id unicos, somente com uma conta conectada.', '\n\nContagem de valores na coluna label (rótulo)',new_dataset_unique.label.value_counts(), '\nNumero de linhas e colunas na base de dados:',new_dataset_unique.shape)
start_timer()

 Existem 18983 user id unicos, somente com uma conta conectada. 

Contagem de valores na coluna label (rótulo) label
0    17983
1     1000
Name: count, dtype: int64 
Numero de linhas e colunas na base de dados: (18983, 552)

# Salvar arquivo em formato excel antes de realizar a limpeza dos dados
new_dataset_unique.to_excel(f'{files_directory}\MBA001_rawdata.xlsx')

# Usar df.  (To use df instead of new_dataset_unique, atribute it to df)
df = new_dataset_unique.copy()

# Verificar nomes das colunas (Check columns name)
print("Columns before drop:", df.columns.to_list(), '\nThe number of rows and columns was:',df.shape)

# Lista de colunas que não serão utilizadas. Drop columns that will not be used. List of columns to drop:
columns_to_drop = ['l1_score', 'l2_score', 'l3_score', 'trace_id', 'query_date', 'account', 'date', 'odds', 'score']

# Remover colunas que não serão utilizadas. Drop the columns
df.drop(columns=columns_to_drop, inplace=True)
print("\nColumns after drop:", df.columns.to_list(), '\nThe number of rows and columns is:',df.shape)

Columns before drop: ['l1_score', 'l2_score', 'l3_score', 'user_id', 'trace_id', 'query_date', 'bank', 'account', 'K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026', 'K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083', 'K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', 'K00209', 'K00210', 'K00211', 'K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268', 'K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280', 'K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369', 'K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465', 'K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483', 'K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540', 'label', 'date', 'odds', 'score'] 
The number of rows and columns was: (18983, 552)

Columns after drop: ['user_id', 'bank', 'K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026', 'K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083', 'K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', 'K00209', 'K00210', 'K00211', 'K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268', 'K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280', 'K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369', 'K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465', 'K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483', 'K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540', 'label'] 
The number of rows and columns is: (18983, 543)

# Verificar 5 linhas do df (Check df)
display(df.head())

# Valores faltantes estão identificados, porem não estão uniforme. Lots of -999999 values, it means 'missing values'. Replace it to nan (not a number)
df.replace([-999999, -999999.0, -999999.00], np.nan, inplace=True)

# df after replace -999999 by nan
display('df após trocar -999999 por NaN:', df.head())

'df após trocar -999999 por NaN:'

# Discretização de 'user_id'. Discretize 'user_id' column and replace it with integer numbers
user_id_mapping = {user_id: i for i, user_id in enumerate(df['user_id'].unique())}
df['user_id'] = df['user_id'].map(user_id_mapping)

# Create a dictionary with user_id and its respective number
# The 'user_id' values are mapped to integer numbers
user_id_dict = {'user_id': list(user_id_mapping.keys()), 'number': list(user_id_mapping.values())}

# Save the dictionary to a text file
file_path = f'{files_directory}/user_id_mapping.txt'
with open(file_path, 'w') as file:
    for key, value in user_id_dict.items():
        file.write(f"{key}: {value}\n")

# Discretização de 'bank'. Discretize 'bank' column and replace it with integer numbers
bank_mapping = {bank: i for i, bank in enumerate(df['bank'].unique())}
df['bank'] = df['bank'].map(bank_mapping)

# Create a dictionary with 'bank' and its respective number
# The 'bank' values are mapped to integer numbers
bank_dict = {'bank': list(bank_mapping.keys()), 'number': list(bank_mapping.values())}

# Save the dictionary to a text file
file_path = f'{files_directory}/bank_mapping.txt'
with open(file_path, 'w') as file:
    for key, value in bank_dict.items():
        file.write(f"{key}: {value}\n")

# Check df
display(df.tail())

display(df.describe())

Dados anônimos, sem informações pessoais oi identificação do banco. Menor consumo de memória e processamento.

# Identifica valor max no df. Find the maximum number in the DataFrame, ignoring NaN ( the ideia is optimize memory usage)
max_value = df.max().max()
print("Maximum value in the DataFrame:", max_value)

# Identifica valor min no df. Find the minimum number in the DataFrame
min_value = df.min().min()
print("Minimum value in the DataFrame:", min_value)

Maximum value in the DataFrame: 1375788.93
Minimum value in the DataFrame: -367104.33

# Memória inicial.  Intial memory usage: was 78.8 MB
print(df.info())

<class 'pandas.core.frame.DataFrame'>
Index: 18983 entries, 0 to 26823
Columns: 543 entries, user_id to label
dtypes: float64(520), int64(23)
memory usage: 78.8 MB
None

# Para economizar memória, alterar tipo de determinadas variaveis. To save memory use the minimal type needed
numeric_columns = df.select_dtypes(include=[ 'float64'])

# Convert the numeric columns to floatXX or intXX
df[numeric_columns.columns] = numeric_columns.astype('float32')
numeric_columns = df.select_dtypes(include=['int64'])
df[numeric_columns.columns] = numeric_columns.astype('int32')

df = df.round(2)

# Print the data types after conversion
print(df.dtypes.to_dict())

# To optimal results you may specify a type to each column

print(df.info())
# Almost half of the memory usage

{'user_id': dtype('int32'), 'bank': dtype('int32'), 'K00001': dtype('float32'), 'K00002': dtype('float32'), 'K00003': dtype('float32'), 'K00004': dtype('float32'), 'K00005': dtype('float32'), 'K00006': dtype('float32'), 'K00007': dtype('float32'), 'K00008': dtype('float32'), 'K00009': dtype('float32'), 'K00010': dtype('float32'), 'K00011': dtype('float32'), 'K00012': dtype('float32'), 'K00013': dtype('float32'), 'K00014': dtype('float32'), 'K00015': dtype('float32'), 'K00016': dtype('float32'), 'K00017': dtype('float32'), 'K00018': dtype('float32'), 'K00019': dtype('float32'), 'K00020': dtype('float32'), 'K00021': dtype('float32'), 'K00022': dtype('float32'), 'K00023': dtype('float32'), 'K00024': dtype('float32'), 'K00025': dtype('float32'), 'K00026': dtype('int32'), 'K00027': dtype('int32'), 'K00028': dtype('float32'), 'K00029': dtype('float32'), 'K00030': dtype('float32'), 'K00031': dtype('float32'), 'K00032': dtype('float32'), 'K00033': dtype('float32'), 'K00034': dtype('float32'), 'K00035': dtype('float32'), 'K00036': dtype('float32'), 'K00037': dtype('float32'), 'K00038': dtype('float32'), 'K00039': dtype('float32'), 'K00040': dtype('float32'), 'K00041': dtype('float32'), 'K00042': dtype('float32'), 'K00043': dtype('float32'), 'K00044': dtype('float32'), 'K00045': dtype('float32'), 'K00046': dtype('float32'), 'K00047': dtype('float32'), 'K00048': dtype('float32'), 'K00049': dtype('float32'), 'K00050': dtype('float32'), 'K00051': dtype('float32'), 'K00052': dtype('float32'), 'K00053': dtype('float32'), 'K00054': dtype('float32'), 'K00055': dtype('float32'), 'K00056': dtype('float32'), 'K00057': dtype('float32'), 'K00058': dtype('float32'), 'K00059': dtype('float32'), 'K00060': dtype('float32'), 'K00061': dtype('float32'), 'K00062': dtype('float32'), 'K00063': dtype('float32'), 'K00064': dtype('float32'), 'K00065': dtype('float32'), 'K00066': dtype('float32'), 'K00067': dtype('float32'), 'K00068': dtype('float32'), 'K00069': dtype('float32'), 'K00070': dtype('float32'), 'K00071': dtype('float32'), 'K00072': dtype('float32'), 'K00073': dtype('float32'), 'K00074': dtype('float32'), 'K00075': dtype('float32'), 'K00076': dtype('float32'), 'K00077': dtype('float32'), 'K00078': dtype('float32'), 'K00079': dtype('float32'), 'K00080': dtype('float32'), 'K00081': dtype('float32'), 'K00082': dtype('float32'), 'K00083': dtype('float32'), 'K00084': dtype('float32'), 'K00085': dtype('float32'), 'K00086': dtype('float32'), 'K00087': dtype('float32'), 'K00088': dtype('float32'), 'K00089': dtype('float32'), 'K00090': dtype('float32'), 'K00091': dtype('float32'), 'K00092': dtype('float32'), 'K00093': dtype('float32'), 'K00094': dtype('float32'), 'K00095': dtype('float32'), 'K00096': dtype('float32'), 'K00097': dtype('float32'), 'K00098': dtype('float32'), 'K00099': dtype('float32'), 'K00100': dtype('float32'), 'K00101': dtype('float32'), 'K00102': dtype('float32'), 'K00103': dtype('float32'), 'K00104': dtype('float32'), 'K00105': dtype('float32'), 'K00106': dtype('float32'), 'K00107': dtype('float32'), 'K00108': dtype('float32'), 'K00109': dtype('float32'), 'K00110': dtype('float32'), 'K00111': dtype('float32'), 'K00112': dtype('float32'), 'K00113': dtype('float32'), 'K00114': dtype('float32'), 'K00115': dtype('float32'), 'K00116': dtype('float32'), 'K00117': dtype('float32'), 'K00118': dtype('float32'), 'K00119': dtype('float32'), 'K00120': dtype('float32'), 'K00121': dtype('float32'), 'K00122': dtype('float32'), 'K00123': dtype('float32'), 'K00124': dtype('float32'), 'K00125': dtype('float32'), 'K00126': dtype('float32'), 'K00127': dtype('float32'), 'K00128': dtype('float32'), 'K00129': dtype('float32'), 'K00130': dtype('float32'), 'K00131': dtype('float32'), 'K00132': dtype('float32'), 'K00133': dtype('float32'), 'K00134': dtype('float32'), 'K00135': dtype('float32'), 'K00136': dtype('float32'), 'K00137': dtype('float32'), 'K00138': dtype('float32'), 'K00139': dtype('float32'), 'K00140': dtype('float32'), 'K00141': dtype('float32'), 'K00142': dtype('float32'), 'K00143': dtype('float32'), 'K00144': dtype('float32'), 'K00145': dtype('float32'), 'K00146': dtype('float32'), 'K00147': dtype('float32'), 'K00148': dtype('float32'), 'K00149': dtype('float32'), 'K00150': dtype('float32'), 'K00151': dtype('float32'), 'K00152': dtype('float32'), 'K00153': dtype('float32'), 'K00154': dtype('float32'), 'K00155': dtype('float32'), 'K00156': dtype('float32'), 'K00157': dtype('float32'), 'K00158': dtype('float32'), 'K00159': dtype('float32'), 'K00160': dtype('float32'), 'K00161': dtype('float32'), 'K00162': dtype('float32'), 'K00163': dtype('float32'), 'K00164': dtype('float32'), 'K00165': dtype('float32'), 'K00166': dtype('float32'), 'K00167': dtype('float32'), 'K00168': dtype('float32'), 'K00169': dtype('float32'), 'K00170': dtype('float32'), 'K00171': dtype('float32'), 'K00172': dtype('float32'), 'K00173': dtype('float32'), 'K00174': dtype('float32'), 'K00175': dtype('float32'), 'K00176': dtype('float32'), 'K00177': dtype('float32'), 'K00178': dtype('float32'), 'K00179': dtype('float32'), 'K00180': dtype('float32'), 'K00181': dtype('float32'), 'K00182': dtype('float32'), 'K00183': dtype('float32'), 'K00184': dtype('float32'), 'K00185': dtype('int32'), 'K00186': dtype('int32'), 'K00187': dtype('int32'), 'K00188': dtype('int32'), 'K00189': dtype('int32'), 'K00190': dtype('int32'), 'K00191': dtype('float32'), 'K00192': dtype('float32'), 'K00193': dtype('float32'), 'K00194': dtype('float32'), 'K00195': dtype('float32'), 'K00196': dtype('float32'), 'K00197': dtype('int32'), 'K00198': dtype('int32'), 'K00199': dtype('int32'), 'K00200': dtype('int32'), 'K00201': dtype('int32'), 'K00202': dtype('int32'), 'K00203': dtype('int32'), 'K00204': dtype('int32'), 'K00205': dtype('int32'), 'K00206': dtype('int32'), 'K00207': dtype('int32'), 'K00208': dtype('int32'), 'K00209': dtype('float32'), 'K00210': dtype('float32'), 'K00211': dtype('float32'), 'K00212': dtype('float32'), 'K00213': dtype('float32'), 'K00214': dtype('float32'), 'K00215': dtype('float32'), 'K00216': dtype('float32'), 'K00217': dtype('float32'), 'K00218': dtype('float32'), 'K00219': dtype('float32'), 'K00220': dtype('float32'), 'K00221': dtype('float32'), 'K00222': dtype('float32'), 'K00223': dtype('float32'), 'K00224': dtype('float32'), 'K00225': dtype('float32'), 'K00226': dtype('float32'), 'K00227': dtype('float32'), 'K00228': dtype('float32'), 'K00229': dtype('float32'), 'K00230': dtype('float32'), 'K00231': dtype('float32'), 'K00232': dtype('float32'), 'K00233': dtype('float32'), 'K00234': dtype('float32'), 'K00235': dtype('float32'), 'K00236': dtype('float32'), 'K00237': dtype('float32'), 'K00238': dtype('float32'), 'K00239': dtype('float32'), 'K00240': dtype('float32'), 'K00241': dtype('float32'), 'K00242': dtype('float32'), 'K00243': dtype('float32'), 'K00244': dtype('float32'), 'K00245': dtype('float32'), 'K00246': dtype('float32'), 'K00247': dtype('float32'), 'K00248': dtype('float32'), 'K00249': dtype('float32'), 'K00250': dtype('float32'), 'K00251': dtype('float32'), 'K00252': dtype('float32'), 'K00253': dtype('float32'), 'K00254': dtype('float32'), 'K00255': dtype('float32'), 'K00256': dtype('float32'), 'K00257': dtype('float32'), 'K00258': dtype('float32'), 'K00259': dtype('float32'), 'K00260': dtype('float32'), 'K00261': dtype('float32'), 'K00262': dtype('float32'), 'K00263': dtype('float32'), 'K00264': dtype('float32'), 'K00265': dtype('float32'), 'K00266': dtype('float32'), 'K00267': dtype('float32'), 'K00268': dtype('float32'), 'K00269': dtype('float32'), 'K00270': dtype('float32'), 'K00271': dtype('float32'), 'K00272': dtype('float32'), 'K00273': dtype('float32'), 'K00274': dtype('float32'), 'K00275': dtype('float32'), 'K00276': dtype('float32'), 'K00277': dtype('float32'), 'K00278': dtype('float32'), 'K00279': dtype('float32'), 'K00280': dtype('float32'), 'K00281': dtype('float32'), 'K00282': dtype('float32'), 'K00283': dtype('float32'), 'K00284': dtype('float32'), 'K00285': dtype('float32'), 'K00286': dtype('float32'), 'K00287': dtype('float32'), 'K00288': dtype('float32'), 'K00289': dtype('float32'), 'K00290': dtype('float32'), 'K00291': dtype('float32'), 'K00292': dtype('float32'), 'K00293': dtype('float32'), 'K00294': dtype('float32'), 'K00295': dtype('float32'), 'K00296': dtype('float32'), 'K00297': dtype('float32'), 'K00298': dtype('float32'), 'K00299': dtype('float32'), 'K00300': dtype('float32'), 'K00301': dtype('float32'), 'K00302': dtype('float32'), 'K00303': dtype('float32'), 'K00304': dtype('float32'), 'K00305': dtype('float32'), 'K00306': dtype('float32'), 'K00307': dtype('float32'), 'K00308': dtype('float32'), 'K00309': dtype('float32'), 'K00310': dtype('float32'), 'K00311': dtype('float32'), 'K00312': dtype('float32'), 'K00313': dtype('float32'), 'K00314': dtype('float32'), 'K00315': dtype('float32'), 'K00316': dtype('float32'), 'K00317': dtype('float32'), 'K00318': dtype('float32'), 'K00319': dtype('float32'), 'K00320': dtype('float32'), 'K00321': dtype('float32'), 'K00322': dtype('float32'), 'K00323': dtype('float32'), 'K00324': dtype('float32'), 'K00325': dtype('float32'), 'K00326': dtype('float32'), 'K00327': dtype('float32'), 'K00328': dtype('float32'), 'K00329': dtype('float32'), 'K00330': dtype('float32'), 'K00331': dtype('float32'), 'K00332': dtype('float32'), 'K00333': dtype('float32'), 'K00334': dtype('float32'), 'K00335': dtype('float32'), 'K00336': dtype('float32'), 'K00337': dtype('float32'), 'K00338': dtype('float32'), 'K00339': dtype('float32'), 'K00340': dtype('float32'), 'K00341': dtype('float32'), 'K00342': dtype('float32'), 'K00343': dtype('float32'), 'K00344': dtype('float32'), 'K00345': dtype('float32'), 'K00346': dtype('float32'), 'K00347': dtype('float32'), 'K00348': dtype('float32'), 'K00349': dtype('float32'), 'K00350': dtype('float32'), 'K00351': dtype('float32'), 'K00352': dtype('float32'), 'K00353': dtype('float32'), 'K00354': dtype('float32'), 'K00355': dtype('float32'), 'K00356': dtype('float32'), 'K00357': dtype('float32'), 'K00358': dtype('float32'), 'K00359': dtype('float32'), 'K00360': dtype('float32'), 'K00361': dtype('float32'), 'K00362': dtype('float32'), 'K00363': dtype('float32'), 'K00364': dtype('float32'), 'K00365': dtype('float32'), 'K00366': dtype('float32'), 'K00367': dtype('float32'), 'K00368': dtype('float32'), 'K00369': dtype('float32'), 'K00370': dtype('float32'), 'K00371': dtype('float32'), 'K00372': dtype('float32'), 'K00373': dtype('float32'), 'K00374': dtype('float32'), 'K00375': dtype('float32'), 'K00376': dtype('float32'), 'K00377': dtype('float32'), 'K00378': dtype('float32'), 'K00379': dtype('float32'), 'K00380': dtype('float32'), 'K00381': dtype('float32'), 'K00382': dtype('float32'), 'K00383': dtype('float32'), 'K00384': dtype('float32'), 'K00385': dtype('float32'), 'K00386': dtype('float32'), 'K00387': dtype('float32'), 'K00388': dtype('float32'), 'K00389': dtype('float32'), 'K00390': dtype('float32'), 'K00391': dtype('float32'), 'K00392': dtype('float32'), 'K00393': dtype('float32'), 'K00394': dtype('float32'), 'K00395': dtype('float32'), 'K00396': dtype('float32'), 'K00397': dtype('float32'), 'K00398': dtype('float32'), 'K00399': dtype('float32'), 'K00400': dtype('float32'), 'K00401': dtype('float32'), 'K00402': dtype('float32'), 'K00403': dtype('float32'), 'K00404': dtype('float32'), 'K00405': dtype('float32'), 'K00406': dtype('float32'), 'K00407': dtype('float32'), 'K00408': dtype('float32'), 'K00409': dtype('float32'), 'K00410': dtype('float32'), 'K00411': dtype('float32'), 'K00412': dtype('float32'), 'K00413': dtype('float32'), 'K00414': dtype('float32'), 'K00415': dtype('float32'), 'K00416': dtype('float32'), 'K00417': dtype('float32'), 'K00418': dtype('float32'), 'K00419': dtype('float32'), 'K00420': dtype('float32'), 'K00421': dtype('float32'), 'K00422': dtype('float32'), 'K00423': dtype('float32'), 'K00424': dtype('float32'), 'K00425': dtype('float32'), 'K00426': dtype('float32'), 'K00427': dtype('float32'), 'K00428': dtype('float32'), 'K00429': dtype('float32'), 'K00430': dtype('float32'), 'K00431': dtype('float32'), 'K00432': dtype('float32'), 'K00433': dtype('float32'), 'K00434': dtype('float32'), 'K00435': dtype('float32'), 'K00436': dtype('float32'), 'K00437': dtype('float32'), 'K00438': dtype('float32'), 'K00439': dtype('float32'), 'K00440': dtype('float32'), 'K00441': dtype('float32'), 'K00442': dtype('float32'), 'K00443': dtype('float32'), 'K00444': dtype('float32'), 'K00445': dtype('float32'), 'K00446': dtype('float32'), 'K00447': dtype('float32'), 'K00448': dtype('float32'), 'K00449': dtype('float32'), 'K00450': dtype('float32'), 'K00451': dtype('float32'), 'K00452': dtype('float32'), 'K00453': dtype('float32'), 'K00454': dtype('float32'), 'K00455': dtype('float32'), 'K00456': dtype('float32'), 'K00457': dtype('float32'), 'K00458': dtype('float32'), 'K00459': dtype('float32'), 'K00460': dtype('float32'), 'K00461': dtype('float32'), 'K00462': dtype('float32'), 'K00463': dtype('float32'), 'K00464': dtype('float32'), 'K00465': dtype('float32'), 'K00466': dtype('float32'), 'K00467': dtype('float32'), 'K00468': dtype('float32'), 'K00469': dtype('float32'), 'K00470': dtype('float32'), 'K00471': dtype('float32'), 'K00472': dtype('float32'), 'K00473': dtype('float32'), 'K00474': dtype('float32'), 'K00475': dtype('float32'), 'K00476': dtype('float32'), 'K00477': dtype('float32'), 'K00478': dtype('float32'), 'K00479': dtype('float32'), 'K00480': dtype('float32'), 'K00481': dtype('float32'), 'K00482': dtype('float32'), 'K00483': dtype('float32'), 'K00484': dtype('float32'), 'K00485': dtype('float32'), 'K00486': dtype('float32'), 'K00487': dtype('float32'), 'K00488': dtype('float32'), 'K00489': dtype('float32'), 'K00490': dtype('float32'), 'K00491': dtype('float32'), 'K00492': dtype('float32'), 'K00493': dtype('float32'), 'K00494': dtype('float32'), 'K00495': dtype('float32'), 'K00496': dtype('float32'), 'K00497': dtype('float32'), 'K00498': dtype('float32'), 'K00499': dtype('float32'), 'K00500': dtype('float32'), 'K00501': dtype('float32'), 'K00502': dtype('float32'), 'K00503': dtype('float32'), 'K00504': dtype('float32'), 'K00505': dtype('float32'), 'K00506': dtype('float32'), 'K00507': dtype('float32'), 'K00508': dtype('float32'), 'K00509': dtype('float32'), 'K00510': dtype('float32'), 'K00511': dtype('float32'), 'K00512': dtype('float32'), 'K00513': dtype('float32'), 'K00514': dtype('float32'), 'K00515': dtype('float32'), 'K00516': dtype('float32'), 'K00517': dtype('float32'), 'K00518': dtype('float32'), 'K00519': dtype('float32'), 'K00520': dtype('float32'), 'K00521': dtype('float32'), 'K00522': dtype('float32'), 'K00523': dtype('float32'), 'K00524': dtype('float32'), 'K00525': dtype('float32'), 'K00526': dtype('float32'), 'K00527': dtype('float32'), 'K00528': dtype('float32'), 'K00529': dtype('float32'), 'K00530': dtype('float32'), 'K00531': dtype('float32'), 'K00532': dtype('float32'), 'K00533': dtype('float32'), 'K00534': dtype('float32'), 'K00535': dtype('float32'), 'K00536': dtype('float32'), 'K00537': dtype('float32'), 'K00538': dtype('float32'), 'K00539': dtype('float32'), 'K00540': dtype('float32'), 'label': dtype('int32')}
<class 'pandas.core.frame.DataFrame'>
Index: 18983 entries, 0 to 26823
Columns: 543 entries, user_id to label
dtypes: float32(520), int32(23)
memory usage: 39.5 MB
None

# Salva o arquivo modificado. Save the cleaned dataframe
df.to_excel(f'{files_directory}/MBA001_cleandata.xlsx')

# df = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=0)

* Outlier Detection using different statistical or machine learning techniques:

robust_z_score(data):

The Z-score measures how many standard deviations a data point is away from the median of the dataset, making it robust to outliers. The values returned are the Z-scores for each data point. A higher Z-score indicates a more extreme deviation from the median.

detect_outliers_isolation_forest(data):

It fits an Isolation Forest model to the input data and predicts whether each data point is an outlier or not. The values returned are typically -1 for outliers and 1 for inliers (non-outliers). So, you get a list of -1s and 1s indicating the outlier status of each data point.

detect_outliers_one_class_svm(data):

It fits a One-Class SVM model to the input data and predicts whether each data point is an outlier or not. The values returned are typically -1 for outliers and 1 for inliers. So, like the Isolation Forest, you get a list of -1s and 1s indicating the outlier status of each data point.

detect_outliers_iqr(data):

It calculates the first quartile (Q1) and third quartile (Q3) of the data and defines a range within which data points are considered normal. Data points falling outside this range are identified as outliers. The values returned are typically boolean (True or False), where True indicates an outlier and False indicates an inlier.

detect_outliers_z_score(data):

It calculates the Z-scores for each data point by standardizing the data (subtracting the mean and dividing by the standard deviation). Data points with absolute Z-scores greater than a specified threshold are considered outliers. The values returned are typically boolean (True or False), where True indicates an outlier and False indicates an inlier.

The first dataframe display the raw results of outlier detection. The second display 1 for outliers and 0 for inliers. The third shows the sum of the five different methods.

from sklearn.ensemble import IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.impute import SimpleImputer

# Identificação de outliers. Outlier detection
# Criação do df outliers_score utilizando 5 metodos distintos. Create the outliers_score DataFrame using 5 different methods
# Apesar desse passo estar sendo realizado agora, idealmente deve ser realizado após a eliminação de variaveis desnecessarias.
# Economizando tempo de processamento. Although this step is being performed now, ideally it should be performed after the elimination of unnecessary variables. Saving processing time.
start_timer()

# Fill missing values with 0
df.fillna(0, inplace=True)

# Specify the range [2:-1] for columns
df_subset = df.iloc[:, 2:-1]
#df_subset = df.iloc[2:-1, :]

# Initialize the results DataFrame
results_df_outliers = pd.DataFrame(index=df_subset.index)

# Define outlier detection functions
def robust_z_score(data):
    median = np.median(data)
    mad = np.median(np.abs(data - median))
    return np.abs(data - median) / mad

def detect_outliers_isolation_forest(data):
    isolation_forest = IsolationForest(contamination=0.10)
    isolation_forest.fit(data)
    return isolation_forest.predict(data)

def detect_outliers_one_class_svm(data):
    one_class_svm = OneClassSVM(nu=0.20)
    one_class_svm.fit(data)
    return one_class_svm.predict(data)

def detect_outliers_iqr(data):
    Q1 = np.percentile(data, 25)
    Q3 = np.percentile(data, 75)
    iqr = Q3 - Q1
    lower_bound = Q1 - 1.5 * iqr
    upper_bound = Q3 + 1.5 * iqr
    return np.where((data < lower_bound) | (data > upper_bound), True, False)

def detect_outliers_z_score(data):
    z_scores = (data - np.mean(data)) / np.std(data)
    threshold_std = 2  # Adjust threshold as needed
    return np.where(np.abs(z_scores) > threshold_std, True, False)

# Initialize an empty dictionary to store results
results_dict = {}

# Iterate through columns and perform outlier detection methods
for col in df_subset.columns:
    column_data = df_subset[col].values

     # Robust Z-Score
    median = np.median(column_data)
    mad = np.median(np.abs(column_data - median))

    # Handle division by zero by checking if mad is non-zero
    if mad != 0:
        robust_z_scores = np.abs(column_data - median) / mad
    else:
        robust_z_scores = np.zeros_like(column_data)

    # Isolation Forest
    outliers_if = detect_outliers_isolation_forest(column_data.reshape(-1, 1))

    # One-Class SVM
    outliers_svm = detect_outliers_one_class_svm(column_data.reshape(-1, 1))

    # Interquartile Range (IQR)
    outliers_iqr = detect_outliers_iqr(column_data)

    # Standard Deviation (Z-Score)
    #outliers_std = detect_outliers_z_score(column_data)

    # Standard Deviation (Z-Score) method
    std_dev = np.std(column_data)
    if std_dev == 0:
        outliers_std = np.zeros_like(column_data)
    else:
        z_scores = (column_data - np.mean(column_data)) / std_dev

        # Convert the Z-Scores to 1 for outliers, 0 for non-outliers
        threshold_std = 1  # Adjust threshold as needed
        outliers_std = np.where(np.abs(z_scores) > threshold_std, 1, 0)

    # Reshape the outliers_std array to be 1-dimensional
    outliers_std = outliers_std.flatten()


    # Store results in the dictionary
    results_dict[f'{col}_RobustZScore'] = robust_z_scores
    results_dict[f'{col}_IsolationForest'] = 0.5 -(outliers_if/2) # Convert -1 to 1 to 0 to 1
    results_dict[f'{col}_OneClassSVM'] = 0.5 -(outliers_svm/2) # Convert -1 to 1 to 0 to 1
    results_dict[f'{col}_IQR'] = outliers_iqr.astype(int)
    results_dict[f'{col}_ZScore'] = outliers_std.astype(int)

# Create the results DataFrame from the dictionary
results_df_outliers = pd.DataFrame(results_dict)

# Display the results
display(results_df_outliers)

# Define a function to map the outlier status to 1 or 0 based on threshold
def map_outlier_status(column, threshold):
    return column.map(lambda x: 0 if x < threshold else 1)

# Define the threshold for robust_z_score and detect_outliers_z_score
robust_z_score_threshold = 3  # Set the desired threshold
z_score_threshold = 3  # Set the desired threshold

# Apply the mapping function to each relevant column
for col_name in results_df_outliers.columns:
    if 'RobustZScore' in col_name:
        results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], robust_z_score_threshold)
    elif 'IsolationForest' in col_name:
        results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
    elif 'OneClassSVM' in col_name:
        results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
    elif 'IQR' in col_name:
        results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
    elif 'ZScore' in col_name:
        results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], z_score_threshold)

# Print updated results_df
display(results_df_outliers)
# Salva o arquivo results_df_outliers
results_df_outliers.to_excel(f'{files_directory}/MBA001_results_df_outliers.xlsx')

# Initialize a new DataFrame to store the summed outlier scores
outliers_score = pd.DataFrame()

# Sum the outlier scores column-wise for each field in the original DataFrame
for field in df_subset.columns:
    field_scores = results_df_outliers.filter(like=field)
    field_sum = field_scores.sum(axis=1)
    outliers_score[field] = field_sum

# Print 'outliers_score' DataFrame
display(outliers_score)
# Salva o arquivo results_df_outliers
outliers_score.to_excel(f'{files_directory}/MBA001_outliers_score.xlsx')
end_timer()

Tempo de execução: 4748.098927497864 segundos

3. Exploração e Análise de Dados¶

df = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=0)
df

# Analise exploratoria inicial. Initial exploratory analysis

# Adiciona a moda de cada variavel ao describe. Add the mode of each variable to the describe
modes = df.mode()
# Percentis com passo de 5% no describe. Percentiles in describe with steps of 5%
describe = round(df.describe(include='all', percentiles=np.arange(0, 1.05, 0.05))).T
describe['mode'] = modes.T[0]
display(describe.head())

describe.columns

Index(['count', 'mean', 'std', 'min', '0%', '5%', '10%', '15%', '20%', '25%',
       '30%', '35%', '40%', '45%', '50%', '55%', '60%', '65%', '70%', '75%',
       '80%', '85%', '90%', '95%', '100%', 'max', 'mode'],
      dtype='object')

# Analise explorátoria abrangente. Comprehensive exploratory analysis

def calculate_woe_iv(data, feature, target):
    """
    Calculate the Weight of Evidence (WOE) and Information Value (IV) for a given feature.

    Parameters:
        data (DataFrame): The input DataFrame containing the feature and target columns.
        feature (str): The name of the feature column (continuous variable).
        target (str): The name of the target column (dependent variable).

    Returns:
        float: Information Value (IV) for the given feature.
    """
    # Cria uma cópia dos dados de entrada para evitar modificar o DataFrame original
    # Create a copy of the input data to avoid modifying the original DataFrame
    data_copy = data.copy()

    # Preenche valores faltantes na coluna feature com zero (não faz sentido pois a informação de valores faltantes seria perdida)
    # Fill missing values in the feature column with zeros
    # data_copy[feature].fillna(0, inplace=True)

    # Calcula o número total de instâncias positivas e negativas na variável alvo
    # Calculate the total number of positive and negative instances in the target variable
    total_positives = data_copy[target].sum()
    total_negatives = data_copy[target].count() - total_positives

    # Lida com caso especial se não houver instâncias positivas ou negativas
    # Handle special case if there are no positive or negative instances
    if total_positives == 0 or total_negatives == 0:
        raise ValueError("The target variable should have both positive and negative instances.")

    # Verifica se existem valores válidos na coluna antes de calcular os quantis
    # Check if there are valid values in the column before calculating quantiles
    if data_copy[feature].notnull().any():
        # Calculate the total number of bins to be used
        # Use the following to create 20 equal-frequency bins:
        data_copy['bins'] = pd.qcut(data_copy[feature], q=20, duplicates='drop')

        # Calcula o número de instâncias positivas e negativas em cada bin
        # Calculate the number of positive and negative instances in each bin
        grouped = data_copy.groupby('bins')
        bin_counts = grouped[target].agg(['sum', 'count'])

        # Calcula o WoE para cada bin e lida com o caso em que o WoE é infinito (log(0))
        # Calculate the WoE for each bin and handle the case where WoE is infinite (log(0))
        bin_counts['woe'] = np.log((bin_counts['sum'] / total_positives) / (bin_counts['count'] / total_negatives))
        bin_counts.replace([np.inf, -np.inf], 0, inplace=True)

        # Calcula o IV para a variável somando as contribuições de cada bin
        # Calculate the IV for the feature by summing up the contributions from each bin
        iv = ((bin_counts['sum'] / total_positives) - (bin_counts['count'] / total_negatives)) * bin_counts['woe']
        iv = iv.sum()
    else:
        # Se não houver valores válidos na coluna, define IV como 0
        # If there are no valid values in the column, set IV to 0
        iv = 0

    return iv

# Calculate IV for each continuous feature in the DataFrame 'df'
iv_values = {}
continuous_features = df.select_dtypes(include=[np.number]).columns.tolist()

for feature in continuous_features:
    iv = calculate_woe_iv(df, feature, 'label')
    iv_values[feature] = iv

# Cria um DataFrame para armazenar as estatísticas de uma analise exploratoria abrangente
# Create a DataFrame to store summary statistics
describe = round(df.describe(include='all', percentiles=np.arange(0, 1.05, 0.05))).T

# Adiciona o valores iguais a zero, número de valores faltantes e % de valores faltantes
# Add Number of number of zeros, Number of missing, and % missing columns
describe['num_zeros'] = (df == 0).sum()
describe['num_NaN'] = df.isnull().sum()
describe['perc_NaN'] = (df.isnull().sum() / len(df)) * 100

# Adiciona a coluna IV ao DataFrame describe
# Add the IV column to the describe DataFrame
describe['IV'] = pd.Series(iv_values)

# Adiciona a coluna Mode ao DataFrame describe
# Add the Mode column to the describe DataFrame
describe['mode'] = df.mode().T[0]

# Reordena as colunas conforme a necessidade
# Reorder the columns as per the requirement
column_order = ['count', 'num_zeros', 'num_NaN', 'perc_NaN',
                'IV', 'mode', 'mean', 'std', 'min', '0%', '5%', '10%', '15%', '20%', '25%',
       '30%', '35%', '40%', '45%', '50%', '55%', '60%', '65%', '70%', '75%',
       '80%', '85%', '90%', '95%', '100%', 'max']
describe = describe[column_order]

# Display the updated describe DataFrame
display(round(describe,2).head(10))

# Salva o arquivo describe em formato excel
describe.to_excel(f'{files_directory}/MBA001_describe.xlsx', index=False)

Faixa de IV	Interpretação
0 até 0.02	Sem poder preditivo
0.02 até 0.1	Poder preditivo fraco
0.1 até 0.3	Poder preditivo moderado
0.3 até 0.5	Poder preditivo forte
0.5 e acima	(Suspeito) Poder preditivo muito forte

Range of IV	Interpretation
0 to 0.02	No predictive power
0.02 to 0.1	Weak predictive power
0.1 to 0.3	Moderate predictive power
0.3 to 0.5	Strong predictive power
0.5 and above	(Suspicious) Very strong predictive power

Os resultados da analise exploratoria abrangente permitem uma melhor compreensão dos dados e das variáveis, bem como a identificação de possíveis problemas e oportunidades para aprimorar o modelo. Nela podemos verificar distribuição dos dados, contagem de valores nulos, contagem de valores zeros, moda, média, mediana, desvio padrão, mínimo, máximo, quartis, por fim, information value (IV) que sugere a importância de cada variável para o modelo.

# Count occurrences of 0 in columns from index 2 to 541
columns_to_count = df.columns[2:542]
count_of_zeros = (df[columns_to_count] == 0).sum()
print('Counts\n', 'Count occurrences of 0 in columns\n',count_of_zeros.to_dict())

# Count occurrences of NaN in columns from index 2 to 541
columns_to_count = df.columns[2:542]
count_of_nans = df[columns_to_count].isna().sum()
print('\nCount occurrences of NaN in columns\n',count_of_nans.to_dict())

# Count occurrences of NaN and zero in columns from index 2 to 541
print('\nCount occurrences of  sum of NaN and zero in columns\n',(count_of_zeros+count_of_nans).to_dict())

Counts
 Count occurrences of 0 in columns
 {'K00001': 685, 'K00002': 719, 'K00003': 685, 'K00004': 685, 'K00005': 15387, 'K00006': 15387, 'K00007': 4, 'K00008': 4, 'K00009': 4, 'K00010': 4, 'K00011': 5989, 'K00012': 5989, 'K00013': 728, 'K00014': 870, 'K00015': 728, 'K00016': 739, 'K00017': 7660, 'K00018': 7660, 'K00019': 5, 'K00020': 5, 'K00021': 5, 'K00022': 5, 'K00023': 8144, 'K00024': 8144, 'K00025': 12488, 'K00026': 2701, 'K00027': 6495, 'K00028': 5666, 'K00029': 102, 'K00030': 362, 'K00031': 485, 'K00032': 358, 'K00033': 361, 'K00034': 4623, 'K00035': 4631, 'K00036': 207, 'K00037': 724, 'K00038': 198, 'K00039': 221, 'K00040': 1228, 'K00041': 1362, 'K00042': 166, 'K00043': 890, 'K00044': 157, 'K00045': 258, 'K00046': 678, 'K00047': 768, 'K00048': 1183, 'K00049': 1439, 'K00050': 1183, 'K00051': 1210, 'K00052': 3909, 'K00053': 3909, 'K00054': 1029, 'K00055': 1836, 'K00056': 1029, 'K00057': 1064, 'K00058': 1918, 'K00059': 1918, 'K00060': 990, 'K00061': 2119, 'K00062': 990, 'K00063': 1229, 'K00064': 1388, 'K00065': 1388, 'K00066': 0, 'K00067': 0, 'K00068': 0, 'K00069': 0, 'K00070': 2871, 'K00071': 2875, 'K00072': 0, 'K00073': 0, 'K00074': 0, 'K00075': 0, 'K00076': 1466, 'K00077': 1524, 'K00078': 0, 'K00079': 0, 'K00080': 0, 'K00081': 0, 'K00082': 911, 'K00083': 943, 'K00084': 1421, 'K00085': 457, 'K00086': 389, 'K00087': 348, 'K00088': 0, 'K00089': 0, 'K00090': 0, 'K00091': 0, 'K00092': 816, 'K00093': 602, 'K00094': 472, 'K00095': 812, 'K00096': 596, 'K00097': 471, 'K00098': 241, 'K00099': 201, 'K00100': 163, 'K00101': 241, 'K00102': 201, 'K00103': 163, 'K00104': 1023, 'K00105': 762, 'K00106': 611, 'K00107': 1018, 'K00108': 754, 'K00109': 609, 'K00110': 295, 'K00111': 241, 'K00112': 214, 'K00113': 295, 'K00114': 241, 'K00115': 214, 'K00116': 1232, 'K00117': 947, 'K00118': 775, 'K00119': 1232, 'K00120': 947, 'K00121': 775, 'K00122': 342, 'K00123': 271, 'K00124': 236, 'K00125': 342, 'K00126': 271, 'K00127': 236, 'K00128': 10651, 'K00129': 10483, 'K00130': 10566, 'K00131': 10638, 'K00132': 10465, 'K00133': 10549, 'K00134': 10712, 'K00135': 11014, 'K00136': 11262, 'K00137': 10711, 'K00138': 11013, 'K00139': 11262, 'K00140': 863, 'K00141': 820, 'K00142': 781, 'K00143': 10957, 'K00144': 11106, 'K00145': 11442, 'K00146': 10957, 'K00147': 11106, 'K00148': 11442, 'K00149': 11048, 'K00150': 11012, 'K00151': 10998, 'K00152': 11048, 'K00153': 11012, 'K00154': 10998, 'K00155': 2444, 'K00156': 2011, 'K00157': 1804, 'K00158': 2444, 'K00159': 2011, 'K00160': 1804, 'K00161': 2205, 'K00162': 1839, 'K00163': 1671, 'K00164': 2205, 'K00165': 1839, 'K00166': 1671, 'K00167': 8829, 'K00168': 8687, 'K00169': 8928, 'K00170': 8829, 'K00171': 8687, 'K00172': 8928, 'K00173': 7966, 'K00174': 7956, 'K00175': 7902, 'K00176': 7966, 'K00177': 7956, 'K00178': 7902, 'K00179': 2273, 'K00180': 1893, 'K00181': 1658, 'K00182': 2273, 'K00183': 1893, 'K00184': 1658, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 11533, 'K00192': 11936, 'K00193': 12430, 'K00194': 11533, 'K00195': 11936, 'K00196': 12430, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 1626, 'K00210': 0, 'K00211': 0, 'K00212': 3762, 'K00213': 4048, 'K00214': 4050, 'K00215': 3762, 'K00216': 4048, 'K00217': 4050, 'K00218': 0, 'K00219': 0, 'K00220': 0, 'K00221': 12645, 'K00222': 13322, 'K00223': 13972, 'K00224': 12645, 'K00225': 13322, 'K00226': 13972, 'K00227': 12558, 'K00228': 13222, 'K00229': 13867, 'K00230': 12558, 'K00231': 13222, 'K00232': 13867, 'K00233': 12593, 'K00234': 13239, 'K00235': 13866, 'K00236': 12593, 'K00237': 13239, 'K00238': 13866, 'K00239': 10916, 'K00240': 10803, 'K00241': 11243, 'K00242': 10915, 'K00243': 10802, 'K00244': 11242, 'K00245': 12260, 'K00246': 12584, 'K00247': 13152, 'K00248': 12258, 'K00249': 12582, 'K00250': 13150, 'K00251': 12475, 'K00252': 13083, 'K00253': 13680, 'K00254': 12475, 'K00255': 13083, 'K00256': 13680, 'K00257': 12408, 'K00258': 12026, 'K00259': 11918, 'K00260': 12408, 'K00261': 12026, 'K00262': 11918, 'K00263': 11825, 'K00264': 11690, 'K00265': 11900, 'K00266': 11822, 'K00267': 11688, 'K00268': 11898, 'K00269': 12548, 'K00270': 13156, 'K00271': 13732, 'K00272': 12548, 'K00273': 13156, 'K00274': 13732, 'K00275': 12597, 'K00276': 13270, 'K00277': 13830, 'K00278': 12597, 'K00279': 13270, 'K00280': 13830, 'K00281': 9, 'K00282': 12, 'K00283': 6, 'K00284': 0, 'K00285': 0, 'K00286': 0, 'K00287': 0, 'K00288': 0, 'K00289': 0, 'K00290': 8589, 'K00291': 9439, 'K00292': 9922, 'K00293': 8589, 'K00294': 9439, 'K00295': 9922, 'K00296': 0, 'K00297': 9922, 'K00298': 7857, 'K00299': 8443, 'K00300': 8739, 'K00301': 7857, 'K00302': 8443, 'K00303': 8739, 'K00304': 0, 'K00305': 8739, 'K00306': 2062, 'K00307': 1797, 'K00308': 1891, 'K00309': 2062, 'K00310': 1797, 'K00311': 1891, 'K00312': 0, 'K00313': 1891, 'K00314': 9644, 'K00315': 10501, 'K00316': 10995, 'K00317': 9644, 'K00318': 10501, 'K00319': 10995, 'K00320': 0, 'K00321': 10995, 'K00322': 9993, 'K00323': 11061, 'K00324': 11741, 'K00325': 9993, 'K00326': 11061, 'K00327': 11741, 'K00328': 0, 'K00329': 11741, 'K00330': 10086, 'K00331': 11175, 'K00332': 11865, 'K00333': 10086, 'K00334': 11175, 'K00335': 11865, 'K00336': 0, 'K00337': 11865, 'K00338': 10081, 'K00339': 11168, 'K00340': 11856, 'K00341': 10081, 'K00342': 11168, 'K00343': 11856, 'K00344': 0, 'K00345': 11856, 'K00346': 10086, 'K00347': 11175, 'K00348': 11865, 'K00349': 10086, 'K00350': 11175, 'K00351': 11865, 'K00352': 0, 'K00353': 11865, 'K00354': 9163, 'K00355': 9868, 'K00356': 10262, 'K00357': 9163, 'K00358': 9868, 'K00359': 10262, 'K00360': 0, 'K00361': 10262, 'K00362': 7894, 'K00363': 8307, 'K00364': 8506, 'K00365': 7882, 'K00366': 8291, 'K00367': 8490, 'K00368': 0, 'K00369': 8507, 'K00370': 14257, 'K00371': 14928, 'K00372': 15471, 'K00373': 14257, 'K00374': 14928, 'K00375': 15471, 'K00376': 13729, 'K00377': 14045, 'K00378': 14357, 'K00379': 13729, 'K00380': 14045, 'K00381': 14357, 'K00382': 13336, 'K00383': 13468, 'K00384': 13659, 'K00385': 13336, 'K00386': 13468, 'K00387': 13659, 'K00388': 14112, 'K00389': 14667, 'K00390': 15104, 'K00391': 14112, 'K00392': 14667, 'K00393': 15104, 'K00394': 11051, 'K00395': 10340, 'K00396': 10054, 'K00397': 11051, 'K00398': 10340, 'K00399': 10054, 'K00400': 14086, 'K00401': 14697, 'K00402': 15180, 'K00403': 14086, 'K00404': 14697, 'K00405': 15180, 'K00406': 13523, 'K00407': 13890, 'K00408': 14202, 'K00409': 13523, 'K00410': 13890, 'K00411': 14202, 'K00412': 13932, 'K00413': 14313, 'K00414': 14670, 'K00415': 13932, 'K00416': 14313, 'K00417': 14670, 'K00418': 14133, 'K00419': 14749, 'K00420': 15184, 'K00421': 14133, 'K00422': 14749, 'K00423': 15184, 'K00424': 14253, 'K00425': 14927, 'K00426': 15474, 'K00427': 14253, 'K00428': 14927, 'K00429': 15474, 'K00430': 14192, 'K00431': 14827, 'K00432': 15337, 'K00433': 14192, 'K00434': 14827, 'K00435': 15337, 'K00436': 14073, 'K00437': 14627, 'K00438': 15076, 'K00439': 14073, 'K00440': 14627, 'K00441': 15076, 'K00442': 13888, 'K00443': 14327, 'K00444': 14659, 'K00445': 13888, 'K00446': 14327, 'K00447': 14659, 'K00448': 14212, 'K00449': 14865, 'K00450': 15398, 'K00451': 14212, 'K00452': 14865, 'K00453': 15398, 'K00454': 10932, 'K00455': 10412, 'K00456': 10206, 'K00457': 10932, 'K00458': 10412, 'K00459': 10206, 'K00460': 13947, 'K00461': 14526, 'K00462': 14940, 'K00463': 13946, 'K00464': 14525, 'K00465': 14940, 'K00466': 14121, 'K00467': 14740, 'K00468': 15266, 'K00469': 14121, 'K00470': 14740, 'K00471': 15266, 'K00472': 14255, 'K00473': 14928, 'K00474': 15477, 'K00475': 14255, 'K00476': 14928, 'K00477': 15477, 'K00478': 13351, 'K00479': 13713, 'K00480': 14102, 'K00481': 13296, 'K00482': 13713, 'K00483': 14102, 'K00484': 0, 'K00485': 0, 'K00486': 0, 'K00487': 0, 'K00488': 0, 'K00489': 0, 'K00490': 0, 'K00491': 0, 'K00492': 0, 'K00493': 6338, 'K00494': 6482, 'K00495': 6771, 'K00496': 6338, 'K00497': 6482, 'K00498': 6771, 'K00499': 0, 'K00500': 6771, 'K00501': 9778, 'K00502': 10646, 'K00503': 11210, 'K00504': 9778, 'K00505': 10646, 'K00506': 11210, 'K00507': 0, 'K00508': 11210, 'K00509': 10071, 'K00510': 11034, 'K00511': 11732, 'K00512': 10071, 'K00513': 11034, 'K00514': 11732, 'K00515': 0, 'K00516': 11732, 'K00517': 8455, 'K00518': 9070, 'K00519': 9381, 'K00520': 8455, 'K00521': 9070, 'K00522': 9381, 'K00523': 0, 'K00524': 9381, 'K00525': 2186, 'K00526': 1641, 'K00527': 1778, 'K00528': 2186, 'K00529': 1641, 'K00530': 1778, 'K00531': 0, 'K00532': 1778, 'K00533': 6677, 'K00534': 6401, 'K00535': 6269, 'K00536': 6677, 'K00537': 6401, 'K00538': 6269, 'K00539': 0, 'K00540': 6269}

Count occurrences of NaN in columns
 {'K00001': 3458, 'K00002': 3458, 'K00003': 3458, 'K00004': 3458, 'K00005': 3458, 'K00006': 3458, 'K00007': 12994, 'K00008': 12994, 'K00009': 12994, 'K00010': 12994, 'K00011': 12994, 'K00012': 12994, 'K00013': 9774, 'K00014': 9774, 'K00015': 9774, 'K00016': 9774, 'K00017': 9774, 'K00018': 9774, 'K00019': 10839, 'K00020': 10839, 'K00021': 10839, 'K00022': 10839, 'K00023': 10839, 'K00024': 10839, 'K00025': 6495, 'K00026': 0, 'K00027': 0, 'K00028': 7117, 'K00029': 15012, 'K00030': 13206, 'K00031': 13207, 'K00032': 13204, 'K00033': 13204, 'K00034': 13204, 'K00035': 13204, 'K00036': 12536, 'K00037': 12540, 'K00038': 12535, 'K00039': 12535, 'K00040': 12535, 'K00041': 12535, 'K00042': 12296, 'K00043': 12301, 'K00044': 12294, 'K00045': 12294, 'K00046': 12294, 'K00047': 12294, 'K00048': 14191, 'K00049': 14191, 'K00050': 14191, 'K00051': 14191, 'K00052': 14191, 'K00053': 14191, 'K00054': 13591, 'K00055': 13591, 'K00056': 13591, 'K00057': 13591, 'K00058': 13591, 'K00059': 13591, 'K00060': 13372, 'K00061': 13372, 'K00062': 13372, 'K00063': 13372, 'K00064': 13372, 'K00065': 13372, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 15374, 'K00071': 15374, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 14620, 'K00077': 14620, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 14362, 'K00083': 14362, 'K00084': 12851, 'K00085': 6701, 'K00086': 6029, 'K00087': 5383, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 6448, 'K00093': 5771, 'K00094': 5120, 'K00095': 6448, 'K00096': 5771, 'K00097': 5120, 'K00098': 6448, 'K00099': 5771, 'K00100': 5120, 'K00101': 6448, 'K00102': 5771, 'K00103': 5120, 'K00104': 6448, 'K00105': 5771, 'K00106': 5120, 'K00107': 6448, 'K00108': 5771, 'K00109': 5120, 'K00110': 6448, 'K00111': 5771, 'K00112': 5120, 'K00113': 6448, 'K00114': 5771, 'K00115': 5120, 'K00116': 6448, 'K00117': 5771, 'K00118': 5120, 'K00119': 6448, 'K00120': 5771, 'K00121': 5120, 'K00122': 6448, 'K00123': 5771, 'K00124': 5120, 'K00125': 6448, 'K00126': 5771, 'K00127': 5120, 'K00128': 6448, 'K00129': 5771, 'K00130': 5120, 'K00131': 6448, 'K00132': 5771, 'K00133': 5120, 'K00134': 6448, 'K00135': 5771, 'K00136': 5120, 'K00137': 6448, 'K00138': 5771, 'K00139': 5120, 'K00140': 6448, 'K00141': 5771, 'K00142': 5120, 'K00143': 6448, 'K00144': 5771, 'K00145': 5120, 'K00146': 6448, 'K00147': 5771, 'K00148': 5120, 'K00149': 6448, 'K00150': 5771, 'K00151': 5120, 'K00152': 6448, 'K00153': 5771, 'K00154': 5120, 'K00155': 6448, 'K00156': 5771, 'K00157': 5120, 'K00158': 6448, 'K00159': 5771, 'K00160': 5120, 'K00161': 6448, 'K00162': 5771, 'K00163': 5120, 'K00164': 6448, 'K00165': 5771, 'K00166': 5120, 'K00167': 6448, 'K00168': 5771, 'K00169': 5120, 'K00170': 6448, 'K00171': 5771, 'K00172': 5120, 'K00173': 6448, 'K00174': 5771, 'K00175': 5120, 'K00176': 6448, 'K00177': 5771, 'K00178': 5120, 'K00179': 6448, 'K00180': 5771, 'K00181': 5120, 'K00182': 6448, 'K00183': 5771, 'K00184': 5120, 'K00185': 0, 'K00186': 0, 'K00187': 0, 'K00188': 0, 'K00189': 0, 'K00190': 0, 'K00191': 6448, 'K00192': 5771, 'K00193': 5120, 'K00194': 6448, 'K00195': 5771, 'K00196': 5120, 'K00197': 0, 'K00198': 0, 'K00199': 0, 'K00200': 0, 'K00201': 0, 'K00202': 0, 'K00203': 0, 'K00204': 0, 'K00205': 0, 'K00206': 0, 'K00207': 0, 'K00208': 0, 'K00209': 15151, 'K00210': 18983, 'K00211': 18983, 'K00212': 14139, 'K00213': 13498, 'K00214': 13279, 'K00215': 14139, 'K00216': 13498, 'K00217': 13279, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 6332, 'K00222': 5647, 'K00223': 4992, 'K00224': 6332, 'K00225': 5647, 'K00226': 4992, 'K00227': 6332, 'K00228': 5647, 'K00229': 4992, 'K00230': 6332, 'K00231': 5647, 'K00232': 4992, 'K00233': 6332, 'K00234': 5647, 'K00235': 4992, 'K00236': 6332, 'K00237': 5647, 'K00238': 4992, 'K00239': 6332, 'K00240': 5647, 'K00241': 4992, 'K00242': 6332, 'K00243': 5647, 'K00244': 4992, 'K00245': 6332, 'K00246': 5647, 'K00247': 4992, 'K00248': 6332, 'K00249': 5647, 'K00250': 4992, 'K00251': 6332, 'K00252': 5647, 'K00253': 4992, 'K00254': 6332, 'K00255': 5647, 'K00256': 4992, 'K00257': 4712, 'K00258': 4029, 'K00259': 3476, 'K00260': 4712, 'K00261': 4029, 'K00262': 3476, 'K00263': 4712, 'K00264': 4029, 'K00265': 3476, 'K00266': 4712, 'K00267': 4029, 'K00268': 3476, 'K00269': 6332, 'K00270': 5647, 'K00271': 4992, 'K00272': 6332, 'K00273': 5647, 'K00274': 4992, 'K00275': 6332, 'K00276': 5647, 'K00277': 4992, 'K00278': 6332, 'K00279': 5647, 'K00280': 4992, 'K00281': 8897, 'K00282': 7808, 'K00283': 7118, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 8897, 'K00291': 7808, 'K00292': 7118, 'K00293': 8897, 'K00294': 7808, 'K00295': 7118, 'K00296': 17040, 'K00297': 7118, 'K00298': 8897, 'K00299': 7808, 'K00300': 7118, 'K00301': 8897, 'K00302': 7808, 'K00303': 7118, 'K00304': 15857, 'K00305': 7118, 'K00306': 8897, 'K00307': 7808, 'K00308': 7118, 'K00309': 8897, 'K00310': 7808, 'K00311': 7118, 'K00312': 9009, 'K00313': 7118, 'K00314': 8897, 'K00315': 7808, 'K00316': 7118, 'K00317': 8897, 'K00318': 7808, 'K00319': 7118, 'K00320': 18113, 'K00321': 7118, 'K00322': 8897, 'K00323': 7808, 'K00324': 7118, 'K00325': 8897, 'K00326': 7808, 'K00327': 7118, 'K00328': 18859, 'K00329': 7118, 'K00330': 8897, 'K00331': 7808, 'K00332': 7118, 'K00333': 8897, 'K00334': 7808, 'K00335': 7118, 'K00336': 18983, 'K00337': 7118, 'K00338': 8897, 'K00339': 7808, 'K00340': 7118, 'K00341': 8897, 'K00342': 7808, 'K00343': 7118, 'K00344': 18974, 'K00345': 7118, 'K00346': 8897, 'K00347': 7808, 'K00348': 7118, 'K00349': 8897, 'K00350': 7808, 'K00351': 7118, 'K00352': 18983, 'K00353': 7118, 'K00354': 8897, 'K00355': 7808, 'K00356': 7118, 'K00357': 8897, 'K00358': 7808, 'K00359': 7118, 'K00360': 17380, 'K00361': 7118, 'K00362': 8897, 'K00363': 7808, 'K00364': 7118, 'K00365': 8897, 'K00366': 7808, 'K00367': 7118, 'K00368': 15608, 'K00369': 7118, 'K00370': 4712, 'K00371': 4029, 'K00372': 3476, 'K00373': 4712, 'K00374': 4029, 'K00375': 3476, 'K00376': 4712, 'K00377': 4029, 'K00378': 3476, 'K00379': 4712, 'K00380': 4029, 'K00381': 3476, 'K00382': 4712, 'K00383': 4029, 'K00384': 3476, 'K00385': 4712, 'K00386': 4029, 'K00387': 3476, 'K00388': 4712, 'K00389': 4029, 'K00390': 3476, 'K00391': 4712, 'K00392': 4029, 'K00393': 3476, 'K00394': 4712, 'K00395': 4029, 'K00396': 3476, 'K00397': 4712, 'K00398': 4029, 'K00399': 3476, 'K00400': 4712, 'K00401': 4029, 'K00402': 3476, 'K00403': 4712, 'K00404': 4029, 'K00405': 3476, 'K00406': 4712, 'K00407': 4029, 'K00408': 3476, 'K00409': 4712, 'K00410': 4029, 'K00411': 3476, 'K00412': 4712, 'K00413': 4029, 'K00414': 3476, 'K00415': 4712, 'K00416': 4029, 'K00417': 3476, 'K00418': 4712, 'K00419': 4029, 'K00420': 3476, 'K00421': 4712, 'K00422': 4029, 'K00423': 3476, 'K00424': 4712, 'K00425': 4029, 'K00426': 3476, 'K00427': 4712, 'K00428': 4029, 'K00429': 3476, 'K00430': 4712, 'K00431': 4029, 'K00432': 3476, 'K00433': 4712, 'K00434': 4029, 'K00435': 3476, 'K00436': 4712, 'K00437': 4029, 'K00438': 3476, 'K00439': 4712, 'K00440': 4029, 'K00441': 3476, 'K00442': 4712, 'K00443': 4029, 'K00444': 3476, 'K00445': 4712, 'K00446': 4029, 'K00447': 3476, 'K00448': 4712, 'K00449': 4029, 'K00450': 3476, 'K00451': 4712, 'K00452': 4029, 'K00453': 3476, 'K00454': 4712, 'K00455': 4029, 'K00456': 3476, 'K00457': 4712, 'K00458': 4029, 'K00459': 3476, 'K00460': 4712, 'K00461': 4029, 'K00462': 3476, 'K00463': 4712, 'K00464': 4029, 'K00465': 3476, 'K00466': 4712, 'K00467': 4029, 'K00468': 3476, 'K00469': 4712, 'K00470': 4029, 'K00471': 3476, 'K00472': 4712, 'K00473': 4029, 'K00474': 3476, 'K00475': 4712, 'K00476': 4029, 'K00477': 3476, 'K00478': 4712, 'K00479': 4029, 'K00480': 3476, 'K00481': 4712, 'K00482': 4029, 'K00483': 3476, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 8886, 'K00494': 7916, 'K00495': 7213, 'K00496': 8886, 'K00497': 7916, 'K00498': 7213, 'K00499': 13984, 'K00500': 7213, 'K00501': 8886, 'K00502': 7916, 'K00503': 7213, 'K00504': 8886, 'K00505': 7916, 'K00506': 7213, 'K00507': 18423, 'K00508': 7213, 'K00509': 8886, 'K00510': 7916, 'K00511': 7213, 'K00512': 8886, 'K00513': 7916, 'K00514': 7213, 'K00515': 18945, 'K00516': 7213, 'K00517': 8886, 'K00518': 7916, 'K00519': 7213, 'K00520': 8886, 'K00521': 7916, 'K00522': 7213, 'K00523': 16594, 'K00524': 7213, 'K00525': 8886, 'K00526': 7916, 'K00527': 7213, 'K00528': 8886, 'K00529': 7916, 'K00530': 7213, 'K00531': 8991, 'K00532': 7213, 'K00533': 8886, 'K00534': 7916, 'K00535': 7213, 'K00536': 8886, 'K00537': 7916, 'K00538': 7213, 'K00539': 13482, 'K00540': 7213}

Count occurrences of  sum of NaN and zero in columns
 {'K00001': 4143, 'K00002': 4177, 'K00003': 4143, 'K00004': 4143, 'K00005': 18845, 'K00006': 18845, 'K00007': 12998, 'K00008': 12998, 'K00009': 12998, 'K00010': 12998, 'K00011': 18983, 'K00012': 18983, 'K00013': 10502, 'K00014': 10644, 'K00015': 10502, 'K00016': 10513, 'K00017': 17434, 'K00018': 17434, 'K00019': 10844, 'K00020': 10844, 'K00021': 10844, 'K00022': 10844, 'K00023': 18983, 'K00024': 18983, 'K00025': 18983, 'K00026': 2701, 'K00027': 6495, 'K00028': 12783, 'K00029': 15114, 'K00030': 13568, 'K00031': 13692, 'K00032': 13562, 'K00033': 13565, 'K00034': 17827, 'K00035': 17835, 'K00036': 12743, 'K00037': 13264, 'K00038': 12733, 'K00039': 12756, 'K00040': 13763, 'K00041': 13897, 'K00042': 12462, 'K00043': 13191, 'K00044': 12451, 'K00045': 12552, 'K00046': 12972, 'K00047': 13062, 'K00048': 15374, 'K00049': 15630, 'K00050': 15374, 'K00051': 15401, 'K00052': 18100, 'K00053': 18100, 'K00054': 14620, 'K00055': 15427, 'K00056': 14620, 'K00057': 14655, 'K00058': 15509, 'K00059': 15509, 'K00060': 14362, 'K00061': 15491, 'K00062': 14362, 'K00063': 14601, 'K00064': 14760, 'K00065': 14760, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 18245, 'K00071': 18249, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 16086, 'K00077': 16144, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 15273, 'K00083': 15305, 'K00084': 14272, 'K00085': 7158, 'K00086': 6418, 'K00087': 5731, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 7264, 'K00093': 6373, 'K00094': 5592, 'K00095': 7260, 'K00096': 6367, 'K00097': 5591, 'K00098': 6689, 'K00099': 5972, 'K00100': 5283, 'K00101': 6689, 'K00102': 5972, 'K00103': 5283, 'K00104': 7471, 'K00105': 6533, 'K00106': 5731, 'K00107': 7466, 'K00108': 6525, 'K00109': 5729, 'K00110': 6743, 'K00111': 6012, 'K00112': 5334, 'K00113': 6743, 'K00114': 6012, 'K00115': 5334, 'K00116': 7680, 'K00117': 6718, 'K00118': 5895, 'K00119': 7680, 'K00120': 6718, 'K00121': 5895, 'K00122': 6790, 'K00123': 6042, 'K00124': 5356, 'K00125': 6790, 'K00126': 6042, 'K00127': 5356, 'K00128': 17099, 'K00129': 16254, 'K00130': 15686, 'K00131': 17086, 'K00132': 16236, 'K00133': 15669, 'K00134': 17160, 'K00135': 16785, 'K00136': 16382, 'K00137': 17159, 'K00138': 16784, 'K00139': 16382, 'K00140': 7311, 'K00141': 6591, 'K00142': 5901, 'K00143': 17405, 'K00144': 16877, 'K00145': 16562, 'K00146': 17405, 'K00147': 16877, 'K00148': 16562, 'K00149': 17496, 'K00150': 16783, 'K00151': 16118, 'K00152': 17496, 'K00153': 16783, 'K00154': 16118, 'K00155': 8892, 'K00156': 7782, 'K00157': 6924, 'K00158': 8892, 'K00159': 7782, 'K00160': 6924, 'K00161': 8653, 'K00162': 7610, 'K00163': 6791, 'K00164': 8653, 'K00165': 7610, 'K00166': 6791, 'K00167': 15277, 'K00168': 14458, 'K00169': 14048, 'K00170': 15277, 'K00171': 14458, 'K00172': 14048, 'K00173': 14414, 'K00174': 13727, 'K00175': 13022, 'K00176': 14414, 'K00177': 13727, 'K00178': 13022, 'K00179': 8721, 'K00180': 7664, 'K00181': 6778, 'K00182': 8721, 'K00183': 7664, 'K00184': 6778, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 17981, 'K00192': 17707, 'K00193': 17550, 'K00194': 17981, 'K00195': 17707, 'K00196': 17550, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 16777, 'K00210': 18983, 'K00211': 18983, 'K00212': 17901, 'K00213': 17546, 'K00214': 17329, 'K00215': 17901, 'K00216': 17546, 'K00217': 17329, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 18977, 'K00222': 18969, 'K00223': 18964, 'K00224': 18977, 'K00225': 18969, 'K00226': 18964, 'K00227': 18890, 'K00228': 18869, 'K00229': 18859, 'K00230': 18890, 'K00231': 18869, 'K00232': 18859, 'K00233': 18925, 'K00234': 18886, 'K00235': 18858, 'K00236': 18925, 'K00237': 18886, 'K00238': 18858, 'K00239': 17248, 'K00240': 16450, 'K00241': 16235, 'K00242': 17247, 'K00243': 16449, 'K00244': 16234, 'K00245': 18592, 'K00246': 18231, 'K00247': 18144, 'K00248': 18590, 'K00249': 18229, 'K00250': 18142, 'K00251': 18807, 'K00252': 18730, 'K00253': 18672, 'K00254': 18807, 'K00255': 18730, 'K00256': 18672, 'K00257': 17120, 'K00258': 16055, 'K00259': 15394, 'K00260': 17120, 'K00261': 16055, 'K00262': 15394, 'K00263': 16537, 'K00264': 15719, 'K00265': 15376, 'K00266': 16534, 'K00267': 15717, 'K00268': 15374, 'K00269': 18880, 'K00270': 18803, 'K00271': 18724, 'K00272': 18880, 'K00273': 18803, 'K00274': 18724, 'K00275': 18929, 'K00276': 18917, 'K00277': 18822, 'K00278': 18929, 'K00279': 18917, 'K00280': 18822, 'K00281': 8906, 'K00282': 7820, 'K00283': 7124, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 17486, 'K00291': 17247, 'K00292': 17040, 'K00293': 17486, 'K00294': 17247, 'K00295': 17040, 'K00296': 17040, 'K00297': 17040, 'K00298': 16754, 'K00299': 16251, 'K00300': 15857, 'K00301': 16754, 'K00302': 16251, 'K00303': 15857, 'K00304': 15857, 'K00305': 15857, 'K00306': 10959, 'K00307': 9605, 'K00308': 9009, 'K00309': 10959, 'K00310': 9605, 'K00311': 9009, 'K00312': 9009, 'K00313': 9009, 'K00314': 18541, 'K00315': 18309, 'K00316': 18113, 'K00317': 18541, 'K00318': 18309, 'K00319': 18113, 'K00320': 18113, 'K00321': 18113, 'K00322': 18890, 'K00323': 18869, 'K00324': 18859, 'K00325': 18890, 'K00326': 18869, 'K00327': 18859, 'K00328': 18859, 'K00329': 18859, 'K00330': 18983, 'K00331': 18983, 'K00332': 18983, 'K00333': 18983, 'K00334': 18983, 'K00335': 18983, 'K00336': 18983, 'K00337': 18983, 'K00338': 18978, 'K00339': 18976, 'K00340': 18974, 'K00341': 18978, 'K00342': 18976, 'K00343': 18974, 'K00344': 18974, 'K00345': 18974, 'K00346': 18983, 'K00347': 18983, 'K00348': 18983, 'K00349': 18983, 'K00350': 18983, 'K00351': 18983, 'K00352': 18983, 'K00353': 18983, 'K00354': 18060, 'K00355': 17676, 'K00356': 17380, 'K00357': 18060, 'K00358': 17676, 'K00359': 17380, 'K00360': 17380, 'K00361': 17380, 'K00362': 16791, 'K00363': 16115, 'K00364': 15624, 'K00365': 16779, 'K00366': 16099, 'K00367': 15608, 'K00368': 15608, 'K00369': 15625, 'K00370': 18969, 'K00371': 18957, 'K00372': 18947, 'K00373': 18969, 'K00374': 18957, 'K00375': 18947, 'K00376': 18441, 'K00377': 18074, 'K00378': 17833, 'K00379': 18441, 'K00380': 18074, 'K00381': 17833, 'K00382': 18048, 'K00383': 17497, 'K00384': 17135, 'K00385': 18048, 'K00386': 17497, 'K00387': 17135, 'K00388': 18824, 'K00389': 18696, 'K00390': 18580, 'K00391': 18824, 'K00392': 18696, 'K00393': 18580, 'K00394': 15763, 'K00395': 14369, 'K00396': 13530, 'K00397': 15763, 'K00398': 14369, 'K00399': 13530, 'K00400': 18798, 'K00401': 18726, 'K00402': 18656, 'K00403': 18798, 'K00404': 18726, 'K00405': 18656, 'K00406': 18235, 'K00407': 17919, 'K00408': 17678, 'K00409': 18235, 'K00410': 17919, 'K00411': 17678, 'K00412': 18644, 'K00413': 18342, 'K00414': 18146, 'K00415': 18644, 'K00416': 18342, 'K00417': 18146, 'K00418': 18845, 'K00419': 18778, 'K00420': 18660, 'K00421': 18845, 'K00422': 18778, 'K00423': 18660, 'K00424': 18965, 'K00425': 18956, 'K00426': 18950, 'K00427': 18965, 'K00428': 18956, 'K00429': 18950, 'K00430': 18904, 'K00431': 18856, 'K00432': 18813, 'K00433': 18904, 'K00434': 18856, 'K00435': 18813, 'K00436': 18785, 'K00437': 18656, 'K00438': 18552, 'K00439': 18785, 'K00440': 18656, 'K00441': 18552, 'K00442': 18600, 'K00443': 18356, 'K00444': 18135, 'K00445': 18600, 'K00446': 18356, 'K00447': 18135, 'K00448': 18924, 'K00449': 18894, 'K00450': 18874, 'K00451': 18924, 'K00452': 18894, 'K00453': 18874, 'K00454': 15644, 'K00455': 14441, 'K00456': 13682, 'K00457': 15644, 'K00458': 14441, 'K00459': 13682, 'K00460': 18659, 'K00461': 18555, 'K00462': 18416, 'K00463': 18658, 'K00464': 18554, 'K00465': 18416, 'K00466': 18833, 'K00467': 18769, 'K00468': 18742, 'K00469': 18833, 'K00470': 18769, 'K00471': 18742, 'K00472': 18967, 'K00473': 18957, 'K00474': 18953, 'K00475': 18967, 'K00476': 18957, 'K00477': 18953, 'K00478': 18063, 'K00479': 17742, 'K00480': 17578, 'K00481': 18008, 'K00482': 17742, 'K00483': 17578, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 15224, 'K00494': 14398, 'K00495': 13984, 'K00496': 15224, 'K00497': 14398, 'K00498': 13984, 'K00499': 13984, 'K00500': 13984, 'K00501': 18664, 'K00502': 18562, 'K00503': 18423, 'K00504': 18664, 'K00505': 18562, 'K00506': 18423, 'K00507': 18423, 'K00508': 18423, 'K00509': 18957, 'K00510': 18950, 'K00511': 18945, 'K00512': 18957, 'K00513': 18950, 'K00514': 18945, 'K00515': 18945, 'K00516': 18945, 'K00517': 17341, 'K00518': 16986, 'K00519': 16594, 'K00520': 17341, 'K00521': 16986, 'K00522': 16594, 'K00523': 16594, 'K00524': 16594, 'K00525': 11072, 'K00526': 9557, 'K00527': 8991, 'K00528': 11072, 'K00529': 9557, 'K00530': 8991, 'K00531': 8991, 'K00532': 8991, 'K00533': 15563, 'K00534': 14317, 'K00535': 13482, 'K00536': 15563, 'K00537': 14317, 'K00538': 13482, 'K00539': 13482, 'K00540': 13482}

# Visualização de contagens de zeros, NaN e soma de ambas as contagens no dataframe
# Visualization of counts of zeros, NaN and sum of both counts in the dataframe

columns_to_count = df.columns[2:542]
column_labels = columns_to_count.tolist()

count_of_zeros = (df[columns_to_count] == 0).sum().astype(int)
count_of_nans = df[columns_to_count].isna().sum().astype(int)
count_of_zeros_and_nans = (count_of_zeros + count_of_nans).astype(int)

# Create the dataframe
data = pd.DataFrame({
    'Columns': column_labels,
    'Count of 0': count_of_zeros,
    'Count of NaN': count_of_nans,
    'Count of 0 + NaN': count_of_zeros_and_nans
})

# Create subplots with three vertical bars
fig = make_subplots(rows=3, cols=1, subplot_titles=('Count of 0', 'Count of NaN', 'Count of 0 + NaN'))

fig.add_trace(go.Bar(
    x=data['Columns'],
    y=data['Count of 0'],
    name='Count of 0',
), row=1, col=1)

fig.add_trace(go.Bar(
    x=data['Columns'],
    y=data['Count of NaN'],
    name='Count of NaN',
), row=2, col=1)

fig.add_trace(go.Bar(
    x=data['Columns'],
    y=data['Count of 0 + NaN'],
    name='Count of 0 + NaN',
), row=3, col=1)

# Set y-axis range to 0 to number of rows in df for each subplot
fig.update_yaxes(range=[0, df.shape[0]], row=1, col=1)
fig.update_yaxes(range=[0, df.shape[0]], row=2, col=1)
fig.update_yaxes(range=[0, df.shape[0]], row=3, col=1)

fig.update_layout(
    title='Counts of 0, NaN, and 0 + NaN occurrences in columns',
    xaxis_title='Columns',
    showlegend=False,
    height=800,
    width=8000
)

fig.show()
# fig to hmtl
pyo.plot(fig, filename=f'{files_directory}/MBA001_Counts_of_0_NaN_and_0_NaN_occurrences_in_columns.html', auto_open=False)

'C:/Temp/Codes/MBA/MBA001_Counts_of_0_NaN_and_0_NaN_occurrences_in_columns.html'

# Count the columns where 'Count of 0 + NaN' == 18983 (total number of rows)
zero_nan_count_columns = data[data['Count of 0 + NaN'] == 18983].shape[0]
print('Number of columns with 18983 zeros and NaNs: {}'.format(zero_nan_count_columns))

# Count the columns where 'Count of 0' == 18983 (total number of rows)
zero_count_columns = data[data['Count of 0'] == 18983].shape[0]
print('Number of columns with 18983 zeros: {}'.format(zero_count_columns))

# Count the columns where 'Count of NaN' == 18983 (total number of rows)
nan_count_columns = data[data['Count of NaN'] == 18983].shape[0]
print('Number of columns with 18983 NaN: {}'.format(nan_count_columns))

# Create a dictionary with index as keys and 'Count of 0 + NaN' counts as values
result_dict_count_zero_nan = {index: count for index, count in zip(data.index, data['Count of 0 + NaN'])}
result_dict_count_zero = {index: count for index, count in zip(data.index, data['Count of 0'])}
result_dict_count_nan = {index: count for index, count in zip(data.index, data['Count of NaN'])}

def write_dict_to_file(data_dict, filename):
    with open(filename, 'w') as f:
        for key, value in data_dict.items():
            f.write(f"{key}: {value}\n")

# Write dictionaries to files
write_dict_to_file(result_dict_count_zero_nan, 'result_dict_count_zero_nan.txt')
write_dict_to_file(result_dict_count_zero, 'result_dict_count_zero.txt')
write_dict_to_file(result_dict_count_nan, 'result_dict_count_nan.txt')

print('Dictionary of counts of zeros and NaNs:', result_dict_count_zero_nan)

Number of columns with 18983 zeros and NaNs: 41
Number of columns with 18983 zeros: 18
Number of columns with 18983 NaN: 4
Dictionary of counts of zeros and NaNs: {'K00001': 4143, 'K00002': 4177, 'K00003': 4143, 'K00004': 4143, 'K00005': 18845, 'K00006': 18845, 'K00007': 12998, 'K00008': 12998, 'K00009': 12998, 'K00010': 12998, 'K00011': 18983, 'K00012': 18983, 'K00013': 10502, 'K00014': 10644, 'K00015': 10502, 'K00016': 10513, 'K00017': 17434, 'K00018': 17434, 'K00019': 10844, 'K00020': 10844, 'K00021': 10844, 'K00022': 10844, 'K00023': 18983, 'K00024': 18983, 'K00025': 18983, 'K00026': 2701, 'K00027': 6495, 'K00028': 12783, 'K00029': 15114, 'K00030': 13568, 'K00031': 13692, 'K00032': 13562, 'K00033': 13565, 'K00034': 17827, 'K00035': 17835, 'K00036': 12743, 'K00037': 13264, 'K00038': 12733, 'K00039': 12756, 'K00040': 13763, 'K00041': 13897, 'K00042': 12462, 'K00043': 13191, 'K00044': 12451, 'K00045': 12552, 'K00046': 12972, 'K00047': 13062, 'K00048': 15374, 'K00049': 15630, 'K00050': 15374, 'K00051': 15401, 'K00052': 18100, 'K00053': 18100, 'K00054': 14620, 'K00055': 15427, 'K00056': 14620, 'K00057': 14655, 'K00058': 15509, 'K00059': 15509, 'K00060': 14362, 'K00061': 15491, 'K00062': 14362, 'K00063': 14601, 'K00064': 14760, 'K00065': 14760, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 18245, 'K00071': 18249, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 16086, 'K00077': 16144, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 15273, 'K00083': 15305, 'K00084': 14272, 'K00085': 7158, 'K00086': 6418, 'K00087': 5731, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 7264, 'K00093': 6373, 'K00094': 5592, 'K00095': 7260, 'K00096': 6367, 'K00097': 5591, 'K00098': 6689, 'K00099': 5972, 'K00100': 5283, 'K00101': 6689, 'K00102': 5972, 'K00103': 5283, 'K00104': 7471, 'K00105': 6533, 'K00106': 5731, 'K00107': 7466, 'K00108': 6525, 'K00109': 5729, 'K00110': 6743, 'K00111': 6012, 'K00112': 5334, 'K00113': 6743, 'K00114': 6012, 'K00115': 5334, 'K00116': 7680, 'K00117': 6718, 'K00118': 5895, 'K00119': 7680, 'K00120': 6718, 'K00121': 5895, 'K00122': 6790, 'K00123': 6042, 'K00124': 5356, 'K00125': 6790, 'K00126': 6042, 'K00127': 5356, 'K00128': 17099, 'K00129': 16254, 'K00130': 15686, 'K00131': 17086, 'K00132': 16236, 'K00133': 15669, 'K00134': 17160, 'K00135': 16785, 'K00136': 16382, 'K00137': 17159, 'K00138': 16784, 'K00139': 16382, 'K00140': 7311, 'K00141': 6591, 'K00142': 5901, 'K00143': 17405, 'K00144': 16877, 'K00145': 16562, 'K00146': 17405, 'K00147': 16877, 'K00148': 16562, 'K00149': 17496, 'K00150': 16783, 'K00151': 16118, 'K00152': 17496, 'K00153': 16783, 'K00154': 16118, 'K00155': 8892, 'K00156': 7782, 'K00157': 6924, 'K00158': 8892, 'K00159': 7782, 'K00160': 6924, 'K00161': 8653, 'K00162': 7610, 'K00163': 6791, 'K00164': 8653, 'K00165': 7610, 'K00166': 6791, 'K00167': 15277, 'K00168': 14458, 'K00169': 14048, 'K00170': 15277, 'K00171': 14458, 'K00172': 14048, 'K00173': 14414, 'K00174': 13727, 'K00175': 13022, 'K00176': 14414, 'K00177': 13727, 'K00178': 13022, 'K00179': 8721, 'K00180': 7664, 'K00181': 6778, 'K00182': 8721, 'K00183': 7664, 'K00184': 6778, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 17981, 'K00192': 17707, 'K00193': 17550, 'K00194': 17981, 'K00195': 17707, 'K00196': 17550, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 16777, 'K00210': 18983, 'K00211': 18983, 'K00212': 17901, 'K00213': 17546, 'K00214': 17329, 'K00215': 17901, 'K00216': 17546, 'K00217': 17329, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 18977, 'K00222': 18969, 'K00223': 18964, 'K00224': 18977, 'K00225': 18969, 'K00226': 18964, 'K00227': 18890, 'K00228': 18869, 'K00229': 18859, 'K00230': 18890, 'K00231': 18869, 'K00232': 18859, 'K00233': 18925, 'K00234': 18886, 'K00235': 18858, 'K00236': 18925, 'K00237': 18886, 'K00238': 18858, 'K00239': 17248, 'K00240': 16450, 'K00241': 16235, 'K00242': 17247, 'K00243': 16449, 'K00244': 16234, 'K00245': 18592, 'K00246': 18231, 'K00247': 18144, 'K00248': 18590, 'K00249': 18229, 'K00250': 18142, 'K00251': 18807, 'K00252': 18730, 'K00253': 18672, 'K00254': 18807, 'K00255': 18730, 'K00256': 18672, 'K00257': 17120, 'K00258': 16055, 'K00259': 15394, 'K00260': 17120, 'K00261': 16055, 'K00262': 15394, 'K00263': 16537, 'K00264': 15719, 'K00265': 15376, 'K00266': 16534, 'K00267': 15717, 'K00268': 15374, 'K00269': 18880, 'K00270': 18803, 'K00271': 18724, 'K00272': 18880, 'K00273': 18803, 'K00274': 18724, 'K00275': 18929, 'K00276': 18917, 'K00277': 18822, 'K00278': 18929, 'K00279': 18917, 'K00280': 18822, 'K00281': 8906, 'K00282': 7820, 'K00283': 7124, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 17486, 'K00291': 17247, 'K00292': 17040, 'K00293': 17486, 'K00294': 17247, 'K00295': 17040, 'K00296': 17040, 'K00297': 17040, 'K00298': 16754, 'K00299': 16251, 'K00300': 15857, 'K00301': 16754, 'K00302': 16251, 'K00303': 15857, 'K00304': 15857, 'K00305': 15857, 'K00306': 10959, 'K00307': 9605, 'K00308': 9009, 'K00309': 10959, 'K00310': 9605, 'K00311': 9009, 'K00312': 9009, 'K00313': 9009, 'K00314': 18541, 'K00315': 18309, 'K00316': 18113, 'K00317': 18541, 'K00318': 18309, 'K00319': 18113, 'K00320': 18113, 'K00321': 18113, 'K00322': 18890, 'K00323': 18869, 'K00324': 18859, 'K00325': 18890, 'K00326': 18869, 'K00327': 18859, 'K00328': 18859, 'K00329': 18859, 'K00330': 18983, 'K00331': 18983, 'K00332': 18983, 'K00333': 18983, 'K00334': 18983, 'K00335': 18983, 'K00336': 18983, 'K00337': 18983, 'K00338': 18978, 'K00339': 18976, 'K00340': 18974, 'K00341': 18978, 'K00342': 18976, 'K00343': 18974, 'K00344': 18974, 'K00345': 18974, 'K00346': 18983, 'K00347': 18983, 'K00348': 18983, 'K00349': 18983, 'K00350': 18983, 'K00351': 18983, 'K00352': 18983, 'K00353': 18983, 'K00354': 18060, 'K00355': 17676, 'K00356': 17380, 'K00357': 18060, 'K00358': 17676, 'K00359': 17380, 'K00360': 17380, 'K00361': 17380, 'K00362': 16791, 'K00363': 16115, 'K00364': 15624, 'K00365': 16779, 'K00366': 16099, 'K00367': 15608, 'K00368': 15608, 'K00369': 15625, 'K00370': 18969, 'K00371': 18957, 'K00372': 18947, 'K00373': 18969, 'K00374': 18957, 'K00375': 18947, 'K00376': 18441, 'K00377': 18074, 'K00378': 17833, 'K00379': 18441, 'K00380': 18074, 'K00381': 17833, 'K00382': 18048, 'K00383': 17497, 'K00384': 17135, 'K00385': 18048, 'K00386': 17497, 'K00387': 17135, 'K00388': 18824, 'K00389': 18696, 'K00390': 18580, 'K00391': 18824, 'K00392': 18696, 'K00393': 18580, 'K00394': 15763, 'K00395': 14369, 'K00396': 13530, 'K00397': 15763, 'K00398': 14369, 'K00399': 13530, 'K00400': 18798, 'K00401': 18726, 'K00402': 18656, 'K00403': 18798, 'K00404': 18726, 'K00405': 18656, 'K00406': 18235, 'K00407': 17919, 'K00408': 17678, 'K00409': 18235, 'K00410': 17919, 'K00411': 17678, 'K00412': 18644, 'K00413': 18342, 'K00414': 18146, 'K00415': 18644, 'K00416': 18342, 'K00417': 18146, 'K00418': 18845, 'K00419': 18778, 'K00420': 18660, 'K00421': 18845, 'K00422': 18778, 'K00423': 18660, 'K00424': 18965, 'K00425': 18956, 'K00426': 18950, 'K00427': 18965, 'K00428': 18956, 'K00429': 18950, 'K00430': 18904, 'K00431': 18856, 'K00432': 18813, 'K00433': 18904, 'K00434': 18856, 'K00435': 18813, 'K00436': 18785, 'K00437': 18656, 'K00438': 18552, 'K00439': 18785, 'K00440': 18656, 'K00441': 18552, 'K00442': 18600, 'K00443': 18356, 'K00444': 18135, 'K00445': 18600, 'K00446': 18356, 'K00447': 18135, 'K00448': 18924, 'K00449': 18894, 'K00450': 18874, 'K00451': 18924, 'K00452': 18894, 'K00453': 18874, 'K00454': 15644, 'K00455': 14441, 'K00456': 13682, 'K00457': 15644, 'K00458': 14441, 'K00459': 13682, 'K00460': 18659, 'K00461': 18555, 'K00462': 18416, 'K00463': 18658, 'K00464': 18554, 'K00465': 18416, 'K00466': 18833, 'K00467': 18769, 'K00468': 18742, 'K00469': 18833, 'K00470': 18769, 'K00471': 18742, 'K00472': 18967, 'K00473': 18957, 'K00474': 18953, 'K00475': 18967, 'K00476': 18957, 'K00477': 18953, 'K00478': 18063, 'K00479': 17742, 'K00480': 17578, 'K00481': 18008, 'K00482': 17742, 'K00483': 17578, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 15224, 'K00494': 14398, 'K00495': 13984, 'K00496': 15224, 'K00497': 14398, 'K00498': 13984, 'K00499': 13984, 'K00500': 13984, 'K00501': 18664, 'K00502': 18562, 'K00503': 18423, 'K00504': 18664, 'K00505': 18562, 'K00506': 18423, 'K00507': 18423, 'K00508': 18423, 'K00509': 18957, 'K00510': 18950, 'K00511': 18945, 'K00512': 18957, 'K00513': 18950, 'K00514': 18945, 'K00515': 18945, 'K00516': 18945, 'K00517': 17341, 'K00518': 16986, 'K00519': 16594, 'K00520': 17341, 'K00521': 16986, 'K00522': 16594, 'K00523': 16594, 'K00524': 16594, 'K00525': 11072, 'K00526': 9557, 'K00527': 8991, 'K00528': 11072, 'K00529': 9557, 'K00530': 8991, 'K00531': 8991, 'K00532': 8991, 'K00533': 15563, 'K00534': 14317, 'K00535': 13482, 'K00536': 15563, 'K00537': 14317, 'K00538': 13482, 'K00539': 13482, 'K00540': 13482}

- As variáveis com 100% de valores zeros ou 100% NaN, podem ser descartadas. Essas colunas não agregam valor ou informação ao modelo.
- Para colunas com 18983 zeros e NaNs altos, é necessário investigar para decidir se deve ser descartado.

- The columns with 100% counts of zeros or 100% NaN, could be dropped. Those columns do not add value or information to the model.
- For columns with 18983 zeros and NaNs high counts, is necessary to investigate to decide if it should be dropped.

# Filter columns with more than 99% of zeros and NaNs
zero_nan_count_great99_columns = data[data['Count of 0 + NaN'] > (18983 * 0.99)].shape[0]

# Create a new dictionary with the count
result_dict_count_zero_nan_great99 = {
    'Number of columns with more than 99% of zeros and NaNs': zero_nan_count_great99_columns
}

write_dict_to_file(result_dict_count_zero_nan_great99, 'zero_nan_count_great99.txt')

# Print the number of columns with more than 99% of zeros and NaNs
print('Number of columns with more than 99% of zeros and NaNs: {}'.format(zero_nan_count_great99_columns))

Number of columns with more than 99% of zeros and NaNs: 139

# Separate rows based on the 'label' column value
df_label_1 = df[df['label'] == 1]
df_label_0 = df[df['label'] == 0]

# Calculate the percentage of missing values for each group and describe the statistics
missing_percent_label_1 = (df_label_1.loc[:, 'K00001':'K00540'].isna().sum(axis=1) / 5.40).describe()
missing_percent_label_0 = (df_label_0.loc[:, 'K00001':'K00540'].isna().sum(axis=1) / 5.40).describe()

print("Missing value percentages for 'label' == 1:")
display(round(missing_percent_label_1, 1))

print("\nMissing value percentages for 'label' == 0:")
display(round(missing_percent_label_0, 1))

# Calculate the difference between 'label' == 0 (The Good) and 'label' == 1 (The Bad) by user_id
result = df.groupby('label').apply(lambda group: (group.loc[:, 'K00001':'K00540'].isna().sum(axis=1)).describe().round(1))
diff_df = result.loc[1] - result.loc[0]
diff_df.name = 'Difference (%)'
result = pd.concat([result, diff_df.to_frame().T])

# Remove the 'count' row
result = result.drop(columns='count')

# Create a horizontal box plot using Plotly
fig = px.box(result.T, orientation='h', title='Difference between Classes by user_id in counts of NaN')
fig.show()

Missing value percentages for 'label' == 1:

count    1000.0
mean       51.7
std        34.1
min         3.9
25%        18.9
50%        40.9
75%        93.7
max        96.3
dtype: float64

Missing value percentages for 'label' == 0:

count    17983.0
mean        37.6
std         31.1
min          2.8
25%         16.5
50%         19.6
75%         59.4
max         96.3
dtype: float64

- É notável que a distribuição de valores em uma classe exibe variações significativas em relação aos valores NaN.
    * MNAR Significa que os dados faltantes estão relacionados sistematicamente aos dados não observados, ou seja, a falta de dados está relacionada a eventos ou fatores que não são medidos, ou seja, erro humano.

- It is noteworthy that the distribution of values in one class exhibits significant variations in relation to NaN values.
    * Missing not at random (MNAR). Data are missing is systematically related to the unobserved data, that is, the missingness is related to events or factors which are not measured, i.e. Human error.

# Calculate the count of zeros per row
count_of_zeros = (df.loc[:, 'K00001':'K00540'] == 0).sum(axis=1)

# Create a horizontal box plot
fig = go.Figure()

# Add box plot for label == 0
fig.add_trace(go.Box(
    x=count_of_zeros[df['label'] == 0],
    orientation='h',
    name='Count of Zeros (label 0)',
))

# Add box plot for label == 1
fig.add_trace(go.Box(
    x=count_of_zeros[df['label'] == 1],
    orientation='h',
    name='Count of Zeros (label 1)',
))

fig.update_layout(
    title='Count of Zeros per Row by Label',
    xaxis_title='Count of Zeros',
    yaxis_title='Label',
)

fig.show()

- A distribuição de valores zero exibe variações significativas entre as classes.
- The distribution of zero values counts in one class exhibits significant variations.

# Convert the 'bank' values to strings
df['bank'] = df['bank'].astype(str)

# Calculate the count of NaN values for each row
df['count_of_nan'] = df.drop(columns=['bank', 'label']).isna().sum(axis=1)

# Calculate the count of zero values for each row
df['count_of_zero'] = (df.drop(columns=['bank', 'label']) == 0).sum(axis=1)

# Calculate the count of NaN + zero values for each row
df['count_of_nan_zero'] = df['count_of_nan'] + df['count_of_zero']

# Create subplots for each unique 'bank' value
fig = make_subplots(rows=1, cols=len(df['bank'].unique()), subplot_titles=df['bank'].unique())

for i, bank_value in enumerate(df['bank'].unique(), 1):
    filtered_df = df[df['bank'] == bank_value]

    fig.add_trace(go.Box(
        x=filtered_df['count_of_nan'],
        y=filtered_df['label'],
        orientation='h',
        name=f'{bank_value} - Count of NaN',
    ), row=1, col=i)

    fig.add_trace(go.Box(
        x=filtered_df['count_of_zero'],
        y=filtered_df['label'],
        orientation='h',
        name=f'{bank_value} - Count of Zero',
    ), row=1, col=i)
'''
    fig.add_trace(go.Box(
        x=filtered_df['count_of_nan_zero'],
        y=filtered_df['label'],
        orientation='h',
        name=f'{bank_value} - Count of NaN + Zero',
    ), row=1, col=i)
'''
# Update layout for the whole figure
fig.update_layout(
    title='Horizontal Box Plots of Count of NaN, Zero, for Each Bank',# and NaN + Zero for Each Bank',
    yaxis_title='Class',
    height=400,  # Adjust the height
    width=6000,  # Adjust the width
)

# Update subplot titles
for i, bank_value in enumerate(df['bank'].unique(), 1):
    fig.update_xaxes(title_text='Count', row=1, col=i)
    fig.update_yaxes(title_text='Class', row=1, col=i)

fig.show()

- The differences in counts of zeros and NaN values are more evident for some banks, like bank: 0, 7, 12, 13, 14

# Convert the 'bank' values to strings
df['bank'] = df['bank'].astype(str)

# Filter the DataFrame to only include rows where 'bank' is equal to 1
df_bank_1 = df[df['bank'] == '0']

# Calculate the count of NaN values for each row
df_bank_1['count_of_nan'] = df_bank_1.drop(columns=['bank', 'label']).isna().sum(axis=1)

# Calculate the count of zero values for each row
df_bank_1['count_of_zero'] = (df_bank_1.drop(columns=['bank', 'label']) == 0).sum(axis=1)

# Calculate the count of NaN + zero values for each row
df_bank_1['count_of_nan_zero'] = df_bank_1['count_of_nan'] + df_bank_1['count_of_zero']

# Create subplots for bank 0
fig = make_subplots(rows=1, cols=1)

fig.add_trace(go.Box(
    x=df_bank_1['count_of_nan'],
    y=df_bank_1['label'],
    orientation='h',  # Horizontal box plot
    name='Count of NaN',
))

fig.add_trace(go.Box(
    x=df_bank_1['count_of_zero'],
    y=df_bank_1['label'],
    orientation='h',  # Horizontal box plot
    name='Count of Zero',
))

'''fig.add_trace(go.Box(
    x=df_bank_1['count_of_nan_zero'],
    y=df_bank_1['label'],
    orientation='h',  # Horizontal box plot
    name='Count of NaN + Zero',
))'''

# Update layout for the whole figure
fig.update_layout(
    title='Count of NaN and Zero for Bank 0', #, and NaN + Zero for Bank 1',
    yaxis_title='Class',
)

# Update x-axis titles
fig.update_xaxes(title_text='Count', row=1, col=1)

# Update y-axis titles
fig.update_yaxes(title_text='Class', row=1, col=1)

fig.show()

# Sum of bank users values
bank_counts = df['bank'].value_counts().head(12)
sum_of_bank_users_values = bank_counts.sum()

print('The number of banks is:', df.bank.nunique(),'\nPercentage of coverage if the banks with more than 140 users are keeped:',round(100*sum_of_bank_users_values/df.shape[0],1),'%', '\nThe number of remaining banks is:',bank_counts.nunique())

# List of banks with more than 140 users
print('\nBanks with more than 140 users:', bank_counts.to_dict())

The number of banks is: 24 
Percentage of coverage if the banks with more than 140 users are keeped: 95.6 % 
The number of remaining banks is: 12

Banks with more than 140 users: {'0': 4952, '3': 3279, '4': 2039, '11': 1861, '5': 1594, '2': 1078, '9': 1061, '6': 909, '1': 806, '8': 223, '10': 181, '13': 168}

- Mantendo apenas os dados correspondentes a 12 bancos específicos, podemos garantir que mais de 95% dos IDs de usuário permaneçam dentro do dataframe. A remoção de dados associados a outros bancos não apenas contribui para a economia de memória e recursos computacionais, mas também aprimora a adequação do conjunto de dados para fins de aprendizado de máquina.

- By retaining only the data corresponding to 12 specific banks, we can ensure that over 95% of user IDs remain within the dataframe. The removal of data associated with other banks not only contributes to memory and computational resource savings but also enhances the suitability of the dataset for machine learning purposes.

# Group the DataFrame by 'bank' and 'label' and calculate the counts
counts_df = df.groupby(['bank', 'label']).size().unstack(fill_value=0)

# Calculate the sum of counts for each 'bank'
counts_df['total_counts'] = counts_df.sum(axis=1)

# Sort the DataFrame by the total counts in descending order
counts_df = counts_df.sort_values(by='total_counts', ascending=False)

# Create a stacked bar graph
fig = go.Figure()

for label in counts_df.columns[:-1]:  # Exclude the last column 'total_counts'
    fig.add_trace(go.Bar(
        x=counts_df.index[0:12],
        y=counts_df[label],
        name=f'Label {label}',
    ))

fig.update_layout(
    title='Bank by Counts of Classes',
    xaxis_title='Bank',
    yaxis_title='Counts',
    barmode='stack',
)

fig.show()

# Group the DataFrame by 'bank' and 'label' and calculate the counts
counts_df = df.groupby(['bank', 'label']).size().unstack(fill_value=0)

counts_df['total_counts'] = counts_df[1] + counts_df[0]

# Calculate the percentage of label '1' over label '0' for each bank
counts_df['percentage_1_over_0'] = round(100*(counts_df[1] /counts_df['total_counts']),1)

#counts_df = counts_df.sort_values(by='percentage_1_over_0', ascending=False)
counts_df = counts_df.sort_values(by='total_counts', ascending=False)

display(counts_df.head(12))

The graph analysis¶

Some variables have high NaN, some case more than 95%. It may be grouped in 4 groups:

K00088 - K000091 (Personal banking): 92.3 96.1 98.8 99.7 - NaN percentual values (%)
- AccountType --- StatsMethod --- StatsPeriod
- Checking Other last_full_month

K00210 - K00211 and K00218 - K00220 (Pre-approved credit and Financial instability): Almost 100%
- Checking Max full_record_period
- Checking Max full_record_period
- Checking+CreditCard Max last_30_days
- Checking+CreditCard Max last_60_days
- Checking+CreditCard Max last_90_days
K00296, K00304, K00320, K00336, K00344, K00352, K00360, K00368 (Income): many with almost 100%
- Checking Tot last_90_days

K00499, K00507, K00515, K00523, K00539 (liabilities): 73,7 97,0 99,8 87,4 71,0 (%)
- Checking Tot last_90_days

Description¶

K00088 - K000091 (Personal banking)¶

- Date of payment
- Date of payment 2, in the case of multiple dates
- Date of payment 3, in the case of multiple dates
- Date of payment 4, in the case of multiple dates

K00210 - K00211 and K00218 - K00220 (Pre-approved credit and Financial instability)¶

- Higher limit .... of in all conected accounts
- Higher limit of personal loan in all conected accounts
-
-
-

K00296, K00304, K00320, K00336, K00344, K00352, K00360, K00368 (Income)¶

- Number of completed months with transactions of salary in last 90 days
- Number of completed months with transactions of business income in last 90 days
- Number of completed months with transactions of bonus in last 90 days
- Number of completed months with transactions of retirement??? in last 90 days
- Number of completed months with transactions of alamony??? in last 90 days
- Number of completed months with transactions of governemental supprot aid??? in last 90 days
- Number of completed months with transactions of other income in last 90 days
- Number of completed months with transactions of .... investment in last 90 days
- Number of completed months with transactions of .... investment in last 90 days

K00499, K00507, K00515, K00523, K00539 (liabilities)¶

- Number of completed months with transactions of credit card in last 90 days
- Number of completed months with transactions of morgage in last 90 days
- Number of completed months with transactions of studant loan...in last 90 days
- Number of completed months with transactions of loan in last 90 days
- Number of months with rental passive and residential bills in last 60 days

Considerations¶

Considering that the variables of type 'Number of completed months with transactions of...' are quantitative and refer to the previous 90, 60, and 30 variables, it is unacceptable that they have a significantly higher number of NaN values compared to the reference variables. This indicates an error in the data preprocessing provided by the company, and for this reason, these features will be discarded. K00296, K00304, K00320, K00336, K00344, K00352, K00360, K00368 (Income). K00499, K00507, K00515, K00523, K00539 (Liabilities).
Furthermore, the variables K00210, K00211, K00218, K00219, and K00220 have 100% NaN values, which renders them uninformative for the model. Consequently, they will also be removed.
Finally, the variables from K00088 to K00091 have been considered to add no value to the model; hence, they will be excluded from the analysis.

Counts of Zeros and NaN¶

Columns with 100% zero values: K00185, K00186, K00187, K00188, K00189, K00190, K00197, K00198, K00199, K00200, K00201, K00202, K00203, K00204, K00205, K00206, K00207, K00208.
- Number of columns with 18983 zeros: 18.
Columns with 100% NaN values: K00210, K00211, K00336, K00352.
- Number of columns with 18983 NaN: 4.

Ação / Action¶

- As variaveis contento 100% de valores zero e 100% de valores NaN serão descartadas.
- Existem 41 colunas que contêm exclusivamente zeros e NaNs. Essas colunas serão mantidas por enquanto.


- These columns consist entirely of zeros or NaNs, which means they do not provide any variability for modeling purposes. Thus, it is advisable to exclude them from consideration.
- Currently, there are 41 columns that contain exclusively zeros and NaNs. These columns will be retained for the time being.

# Mover coluna label para o final, manter user_id e bank no inicio
# Move column label to last column, keep user_id and bank at the beggining
df = df[df.columns.difference(['label']).tolist() + ['label']]
df = df[['user_id', 'bank'] + [col for col in df.columns if col not in ['user_id', 'bank']]]

# Muda o tipo da coluna bank para categorica
# Change type of column bank to category
df['bank'] = df['bank'].astype('category')

display(df.head())

# Lista de variaveis que serão removidas
# List of K features to be dropped
# All features to be dropped going to be in that cell.
display("Df inicial", df.shape, df.head())
k_features_to_drop = [
    'K00296', 'K00304', 'K00320', 'K00336', 'K00344', 'K00352', 'K00360', 'K00368', # Provavel MNAR
    'K00499', 'K00507', 'K00515', 'K00523', 'K00539', # Provavel MNAR
    'K00210', 'K00211', 'K00218', 'K00219', 'K00220', # Praticamente NaN. Pre-approved credit and Financial instability
    'K00088', 'K00089', 'K00090', 'K00091', #
    'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', # Only zeros or only NaNs
    'K00011', 'K00012', 'K00023', 'K00024', 'K00025', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00337', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00353', #Correlation equal NaN
    'K00008', 'K00009', 'K00010', 'K00020', 'K00021', 'K00022' # Equal correlation value
    ]

# Drop the specified K features from the DataFrame
df_droped_001 = df.drop(columns=k_features_to_drop).copy()
display("Df pós drop", df_droped_001.shape, df_droped_001.head(), "Numero de colunas removidas:", len(k_features_to_drop))

# Salva o df após a remoção das colunas
# Save the df after droping the columns
df_droped_001.to_csv(f'{files_directory}/df_droped_001.csv', index=False)

'Df inicial'

(18983, 546)

'Df pós drop'

(18983, 481)

'Numero de colunas removidas:'

65

* Algumas variaveis removidas são altamente correlacionadas com outras variaveis. Isso será discutido a seguir. Para facilitar a visualiazação e controle, foram agrupadas nessa célula.
* Some features are highly correlated with others.

# Verifica a correlação entre as variaveis e rotulos utilizando 3 metodos distintos 'pearson', 'kendall' e 'spearman'
# Check the correlation with labels using 3 different methods 'pearson', 'kendall' and 'spearman'
correlation_methods = ['pearson', 'kendall', 'spearman']
sum_of_corr = {}
sum_of_corr_abs = {}

for method in correlation_methods:
    # Create a dataFrame containing correlation values with columns as feature names
    correlation_with_label = df_droped_001.drop(columns='user_id').corr(method=method)['label']

    # Fill NaN values to avoid errors NaN values are caused by features with 0 variance
    #correlation_with_label = correlation_with_label.fillna(0)

    highest_correlations = correlation_with_label.nlargest(50)
    lowest_correlations = correlation_with_label.nsmallest(50)

    highest_correlations_df = pd.DataFrame(highest_correlations)
    lowest_correlations_df = pd.DataFrame(lowest_correlations)

    # Save the top 50 highest correlations to a text file
    with open(f'{files_directory}/top_50_highest_correlations_{method}.txt', 'w') as f:
        f.write(f"Top 50 highest correlations ({method}):\n")
        f.write(highest_correlations_df.to_string())

    # Save the top 50 lowest correlations to a text file
    with open(f'{files_directory}/top_50_lowest_correlations_{method}.txt', 'w') as f:
        f.write(f"Top 50 lowest correlations ({method}):\n")
        f.write(lowest_correlations_df.to_string())

    # Print the DataFrames
    #print(f"\nTop 50 highest correlations ({method}):")
    #print(highest_correlations_df)

    #print(f"\nTop 50 lowest correlations ({method}):")
    #print(lowest_correlations_df)

    # Calculate the sum of correlation values for each feature
    sum_of_corr[method] = correlation_with_label.drop('label').sum()
    # Calculate the sum of absolute correlation values for each feature
    sum_of_corr_abs[method] = correlation_with_label.drop('label').abs().sum()

# Print the sums of correlation values
print("\nSum of correlation values for each feature:")
print(sum_of_corr)

# Print the sums of absolute correlation values
print("\nSum of absolute correlation values for each feature:")
print(sum_of_corr_abs)

Sum of correlation values for each feature:
{'pearson': -3.7681564988963743, 'kendall': -7.932923432420395, 'spearman': -9.192178821040425}

Sum of absolute correlation values for each feature:
{'pearson': 8.544891749416726, 'kendall': 11.173423231243863, 'spearman': 12.712738085766198}

    - The lowest value of sum of **Pearson's** correlation indicates that the label may not have a strong linear correlation with features

Sum of correlation values for each feature:

{'pearson': -3.814018933618482, 'kendall': -8.073666291772568, 'spearman': -9.363610363899483}

Sum of absolute correlation values for each feature:

{'pearson': 8.602427957374346, 'kendall': 11.319881279648547, 'spearman': 12.891169092747404}

# Correlação entre as variaveis
# Correlation of features
start_timer()
for method in correlation_methods:
    print(f"Correlation Method: {method}")

    # Calculate the correlation matrix
    correlation_matrix = df_droped_001.drop(columns=['user_id']).corr(method=method)

    # Save the correlation matrix to a DataFrame
    correlation_matrix_df = pd.DataFrame(correlation_matrix)

    # Add a new column for the sum of each row
    correlation_matrix_df['Sum'] = correlation_matrix_df.sum(axis=1)

    # Add a new column for the sum of absolute values of each row
    correlation_matrix_df['Sum_abs'] = correlation_matrix_df.abs().sum(axis=1)

    # Print the correlation matrix DataFrame
    #print("Correlation Matrix:")
    display(correlation_matrix_df.head())

    # Save the correlation matrix DataFrame to a CSV file
    correlation_matrix_df.to_csv(f'{files_directory}/correlation_matrix_{method}.csv', index=True)
    end_timer()

Correlation Method: pearson

Correlation Method: kendall

Correlation Method: spearman

# Create a dataframe of sum of correlations for each method
df_sum_of_corrs = pd.DataFrame()

for method in correlation_methods:
    # Load the correlation matrix DataFrame from the CSV file
    correlation_matrix_df = pd.read_csv(f'{files_directory}/correlation_matrix_{method}.csv', index_col=0)

    # Add columns Sum and Sum_abs to the correlation_matrix_df DataFrame for each method
    df_sum_of_corrs['Sum_' + method] = correlation_matrix_df['Sum'].round(1)
    df_sum_of_corrs['Sum_abs_' + method] = correlation_matrix_df['Sum_abs'].round(1)

# Save the df_sum_of_corrs DataFrame to a CSV file
df_sum_of_corrs.to_csv(f'{files_directory}/df_sum_of_corrs.csv', index=True)
display(df_sum_of_corrs.tail())

Correlações¶

O coeficiente de correlação de Pearson mede a relação linear entre duas variáveis contínuas.

Varia de -1 a +1, onde -1 indica uma correlação linear negativa perfeita, 1 indica uma correlação linear positiva perfeita e 0 indica nenhuma correlação linear.
Assume que os dados são distribuídos normalmente e relacionados linearmente.
Adequado para avaliar relacionamentos lineares entre recursos contínuos e a variável de destino na modelagem de risco de crédito.
Pode não capturar relacionamentos não lineares, o que pode ser importante na seleção de recursos.

A correlação de Spearman classifica a relação monotônica entre variáveis.

Calcula a correlação com base nas classificações dos dados, não nos valores reais.
Varia de -1 a +1, semelhante a Pearson, mas mede a força e a direção de uma associação monotônica, não necessariamente linear.
Útil para capturar relacionamentos monotônicos que podem não ser estritamente lineares, o que pode ser relevante na modelagem de risco de crédito.
Pode ser menos sensível a outliers em comparação com a correlação de Pearson.

Tau de Kendall é outra medida de correlação baseada em classificação.

Quantifica a semelhança das ordenações dos pontos de dados entre duas variáveis.
Como Spearman, avalia a força e a direção de um relacionamento monotônico.
Particularmente útil ao lidar com dados categóricos ou ordinais, onde a suposição de linearidade pode não ser válida.
Adequado para capturar relacionamentos entre recursos e variável de destino na modelagem de risco de crédito quando a natureza do relacionamento não é necessariamente linear.
Na modelagem de risco de crédito e seleção de recursos, a escolha do método de correlação depende da natureza dos dados e dos relacionamentos entre as variáveis. Por exemplo:
- Se os relacionamentos forem lineares e distribuídos normalmente, a correlação de Pearson pode ser apropriada.
- Se os relacionamentos forem monotônicos, mas não necessariamente lineares, Spearman ou correlação de Kendall podem ser melhores escolhas.
- Ao lidar com tipos de dados mistos (categóricos, ordinais, contínuos), Kendall tau pode ser mais adequado devido à sua natureza não paramétrica.
- Devido aos tipos de dados mistos, a natureza do relacionamento entre recursos e destino não é necessariamente linear e a distribuição não homogênea, Kendall deve ser mais apropriada para lidar com o conjunto de dados klavi.

Correlations¶

Pearson correlation coefficient measures the linear relationship between two continuous variables.

It ranges from -1 to +1, where -1 indicates a perfect negative linear correlation, 1 indicates a perfect positive linear correlation, and 0 indicates no linear correlation.
It assumes that the data is normally distributed and linearly related.
Suitable for assessing linear relationships between continuous features and the target variable in credit risk modeling.
May not capture non-linear relationships well, which can be important in feature selection. Spearman Rank Correlation:

Spearman's rank correlation assesses the monotonic relationship between variables.

It computes the correlation based on the ranks of the data rather than the actual values.
It ranges from -1 to +1, similar to Pearson, but it measures the strength and direction of a monotonic association, not necessarily linear.
Useful for capturing monotonic relationships that may not be strictly linear, which can be relevant in credit risk modeling.
Can be less sensitive to outliers compared to Pearson correlation. Kendall Tau Correlation:

Kendall's tau is another rank-based correlation measure.

It quantifies the similarity of the orderings of the data points between two variables.
Like Spearman, it assesses the strength and direction of a monotonic relationship.
Particularly useful when dealing with categorical or ordinal data, where the assumption of linearity may not hold.
Suitable for capturing relationships between features and target variable in credit risk modeling when the nature of the relationship is not necessarily linear.
In credit risk modeling and feature selection, the choice of correlation method depends on the nature of the data and the relationships between variables. For example:
- If the relationships are linear and normally distributed, Pearson correlation might be appropriate.
- If the relationships are monotonic but not necessarily linear, Spearman or Kendall correlation could be better choices.
- When dealing with mixed data types (categorical, ordinal, continuous), Kendall tau might be more suitable due to its non-parametric nature.
- Due to mixed data types, nature of the relationship features target is not necessarily linear and non homogeneous distribution Kendall should be more appropriate to deal with klavi dataset.

# Create a DataFrame to store correlation results
correlation_methods = ['pearson', 'spearman', 'kendall']
correlation_results = pd.DataFrame()

# Iterate over each correlation method
for method in correlation_methods:
    correlation_matrix_label_0 = df_droped_001[df_droped_001['label'] == 0].corr(method=method)
    correlation_matrix_label_1 = df_droped_001[df_droped_001['label'] == 1].corr(method=method)

    # Calculate the sum of correlations for each class
    sum_corr_label_0 = correlation_matrix_label_0.sum()
    sum_corr_label_1 = correlation_matrix_label_1.sum()

    # Calculate the difference in sum of correlations between classes
    diff_sum_corr = sum_corr_label_0 - sum_corr_label_1

    # Create a dictionary for the correlation results
    correlation_result = {
        f'Corr_{method}_label_0': sum_corr_label_0,
        f'Corr_{method}_label_1': sum_corr_label_1,
        f'Diff_Corr_{method}': diff_sum_corr
    }

    # Append the dictionary to the correlation_results DataFrame
    correlation_results = pd.concat([correlation_results, pd.DataFrame(correlation_result)], axis=1)

# Save the correlation results DataFrame to a CSV file
correlation_results.to_csv(f'{files_directory}/correlation_results_with_labels_0_1.csv', index=False)

# Display the correlation results DataFrame
display(correlation_results.head())

"\n# Iterate over each correlation method\nfor method in correlation_methods:\n    correlation_matrix_label_0 = df_droped_001[df_droped_001['label'] == 0].corr(method=method)\n    correlation_matrix_label_1 = df_droped_001[df_droped_001['label'] == 1].corr(method=method)\n    \n    # Calculate the sum of correlations for each class\n    sum_corr_label_0 = correlation_matrix_label_0.sum()\n    sum_corr_label_1 = correlation_matrix_label_1.sum()\n    \n    # Calculate the difference in sum of correlations between classes\n    diff_sum_corr = sum_corr_label_0 - sum_corr_label_1\n    \n    # Create a dictionary for the correlation results\n    correlation_result = {\n        f'Corr_{method}_label_0': sum_corr_label_0,\n        f'Corr_{method}_label_1': sum_corr_label_1,\n        f'Diff_Corr_{method}': diff_sum_corr\n    }\n    \n    # Append the dictionary to the correlation_results DataFrame\n    correlation_results = pd.concat([correlation_results, pd.DataFrame(correlation_result)], axis=1)\n\n# Save the correlation results DataFrame to a CSV file\ncorrelation_results.to_csv(f'{files_directory}/correlation_results_with_labels_0_1.csv', index=False)\n\n# Display the correlation results DataFrame\ndisplay(correlation_results.head())"

correlation_results.describe().round(0)

A diferença de valores de correlações de cada sugere que as classes podem ser separáveis e o melhor método é kendall (menor desvio padrão e média intermediária). Isso indica que o método é eficaz para seleção de recursos.
O método da Pearson é o pior (baixa diferença média e alta std)
A normalização dos valores pode ser recomendada para melhor interpretação

.

Those difference in values of correlations of each suggest that classes could be separable and the best method is kendall (lowest standard deviation and mid mean ). It indicates that the method is effective for feature selection
Pearson method is the worse (low mean diff and high std)
Normalization of values may be reccomended for better interpretation

# Cria mapa de calor com as correlções para cada método
# Create a heatmap of correlations for each method

# Drop bank column
df_droped_001_filled_0 = df_droped_001.drop(columns=['bank']).copy()
# Fill NaN values with 0.
df_droped_001_filled_0 = df_droped_001_filled_0.fillna(0) # Isso resulta em uma matriz de correlação diferente da matriz de correlação original

for method in correlation_methods:
    plt.figure(figsize=(150, 140))
    sns.heatmap(df_droped_001_filled_0.corr(method=method), annot=False, cmap='coolwarm', fmt=".2f")
    plt.title(f'Correlation Heatmap ({method.capitalize()})')
    print(f'Correlation Heatmap ({method.capitalize()})')
    # Save the heatmap as a PNG file
    plt.savefig(f'{files_directory}/correlation_matrix_{method}_heatmap.png')

    # Show the heatmap
    plt.show()

Correlation Heatmap (Pearson)

Correlation Heatmap (Spearman)

Correlation Heatmap (Kendall)

Done¶

High Correlation and Feature Selection:

A high correlation among features could indicate redundancy or multicollinearity. This suggests that some features might be redundant and could potentially be dropped to simplify the model and reduce noise.

Diverse Correlation Values:

Different correlation values by method is insightful. It indicates that the relationships between features and the target variable are not purely linear. This highlights the importance of considering different correlation methods to capture both linear and non-linear relationships.

Information Value (IV):

Information Value (IV), is a great addition. IV is a powerful metric in the context of feature selection for classification models. It helps to evaluate the predictive power of features and can guide decisions on whether to keep or drop specific features.

Specific Example:

Features K00338 to K00345 being highly correlated among themselves but having low correlation with other features is an excellent observation. This suggests that they might capture similar information and might not contribute much individually to the model.

Weight of evidence (WOE) by transforming continuous variables into categorical features. The continuous variable is split into bins and based on their WOE , new variables are created. Also, Information Value (IV) helps to determine which feature is useful in prediction. The information value for the independent variables are indicted below. Variables with IV less than 0.02 will not be included in the model because they have no prediction power. -Siddiqi(2006)

# WOE: Weight of Evidence (WOE) and Information Value (IV)

def calculate_woe_iv(data, feature, target):
    """
    Calculate the Weight of Evidence (WOE) and Information Value (IV) for a given feature.

    Parameters:
        data (DataFrame): The input DataFrame containing the feature and target columns.
        feature (str): The name of the feature column (continuous variable).
        target (str): The name of the target column (dependent variable).

    Returns:
        float: Information Value (IV) for the given feature.
    """
    # Create a copy of the input data to avoid modifying the original DataFrame
    data_copy = df_droped_001_filled_0.copy()

    # Fill missing values in the feature column with zeros
    #data_copy[feature].fillna(0, inplace=True)

    # Calculate the total number of positive and negative instances in the target variable
    total_positives = data_copy[target].sum()
    total_negatives = data_copy[target].count() - total_positives

    # Handle special case if there are no positive or negative instances
    if total_positives == 0 or total_negatives == 0:
        raise ValueError("The target variable should have both positive and negative instances.")

    # Calculate the total number of bins to be used
    # Use the following to create 20 equal-frequency bins:
    data_copy['bins'] = pd.qcut(data_copy[feature], q=20, duplicates='drop')

    # Calculate the number of positive and negative instances in each bin
    grouped = data_copy.groupby('bins')
    bin_counts = grouped[target].agg(['sum', 'count'])

    # Calculate the WoE for each bin and handle the case where WoE is infinite (log(0))
    bin_counts['woe'] = np.log((bin_counts['sum'] / total_positives) / (bin_counts['count'] / total_negatives))
    bin_counts.replace([np.inf, -np.inf], 0, inplace=True)

    # Calculate the IV for the feature by summing up the contributions from each bin
    iv = ((bin_counts['sum'] / total_positives) - (bin_counts['count'] / total_negatives)) * bin_counts['woe']
    iv = iv.sum()

    return iv


# Calculate IV for each continuous feature in the DataFrame 'df'
iv_values = {}
continuous_features = df_droped_001_filled_0.select_dtypes(include=[np.number]).columns.tolist()

for feature in continuous_features:
    iv = calculate_woe_iv(df_droped_001_filled_0, feature, 'label')
    iv_values[feature] = iv

# Save the IV values to a text file
with open('IV_values.txt', 'w') as file:
    for feature, iv in iv_values.items():
        file.write(f"Information Value (IV) for {feature}: {iv}\n")

# Create the DataFrame df_IV_001
data = {'Feature': list(iv_values.keys()), 'IV': list(iv_values.values())}
df_IV_001 = pd.DataFrame(data)

# Display the DataFrame df_IV_001
print(df_IV_001)

               Feature        IV
0              user_id  0.020526
1               K00001  0.091833
2               K00002  0.090353
3               K00003  0.091075
4               K00004  0.091174
5               K00005  0.003009
6               K00006  0.003009
7               K00007  0.028465
8               K00013  0.057652
9               K00014  0.059134
10              K00015  0.054569
11              K00016  0.051786
12              K00017  0.008913
13              K00018  0.007743
14              K00019  0.046770
15              K00026  0.003090
16              K00027  0.022579
17              K00028  0.006833
18              K00029  0.046416
19              K00030  0.120599
20              K00031  0.117248
21              K00032  0.125969
22              K00033  0.129081
23              K00034  0.027625
24              K00035  0.035197
25              K00036  0.128550
26              K00037  0.115540
27              K00038  0.125732
28              K00039  0.128030
29              K00040  0.105282
30              K00041  0.105304
31              K00042  0.135852
32              K00043  0.110997
33              K00044  0.128731
34              K00045  0.133440
35              K00046  0.118222
36              K00047  0.114272
37              K00048  0.044286
38              K00049  0.030771
39              K00050  0.040749
40              K00051  0.043353
41              K00052  0.003009
42              K00053  0.003009
43              K00054  0.065690
44              K00055  0.029135
45              K00056  0.057856
46              K00057  0.052124
47              K00058  0.065017
48              K00059  0.052508
49              K00060  0.049522
50              K00061  0.027951
51              K00062  0.047493
52              K00063  0.040768
53              K00064  0.053086
54              K00065  0.054384
55              K00066  0.065106
56              K00067  0.057558
57              K00068  0.061027
58              K00069  0.061064
59              K00070  0.003009
60              K00071  0.003009
61              K00072  0.076059
62              K00073  0.062009
63              K00074  0.072305
64              K00075  0.068626
65              K00076  0.053683
66              K00077  0.035929
67              K00078  0.060682
68              K00079  0.050290
69              K00080  0.061969
70              K00081  0.061371
71              K00082  0.044993
72              K00083  0.046491
73              K00084  0.048754
74              K00085  0.133490
75              K00086  0.133494
76              K00087  0.130009
77              K00092  0.230129
78              K00093  0.237117
79              K00094  0.230759
80              K00095  0.171099
81              K00096  0.180354
82              K00097  0.176811
83              K00098  0.216294
84              K00099  0.217680
85              K00100  0.226909
86              K00101  0.127636
87              K00102  0.140002
88              K00103  0.135174
89              K00104  0.230779
90              K00105  0.239790
91              K00106  0.224047
92              K00107  0.165097
93              K00108  0.179036
94              K00109  0.183326
95              K00110  0.217470
96              K00111  0.213073
97              K00112  0.209657
98              K00113  0.122427
99              K00114  0.138751
100             K00115  0.129169
101             K00116  0.265698
102             K00117  0.293708
103             K00118  0.257652
104             K00119  0.176604
105             K00120  0.194380
106             K00121  0.189049
107             K00122  0.228140
108             K00123  0.226998
109             K00124  0.218431
110             K00125  0.136065
111             K00126  0.142363
112             K00127  0.131074
113             K00128  0.024510
114             K00129  0.040023
115             K00130  0.033256
116             K00131  0.018936
117             K00132  0.031464
118             K00133  0.046961
119             K00134  0.007983
120             K00135  0.009672
121             K00136  0.011919
122             K00137  0.005617
123             K00138  0.003249
124             K00139  0.003591
125             K00140  0.139547
126             K00141  0.139200
127             K00142  0.132486
128             K00143  0.034890
129             K00144  0.057567
130             K00145  0.052723
131             K00146  0.016109
132             K00147  0.037724
133             K00148  0.047565
134             K00149  0.006490
135             K00150  0.016956
136             K00151  0.016593
137             K00152  0.005365
138             K00153  0.006152
139             K00154  0.010536
140             K00155  0.245358
141             K00156  0.262254
142             K00157  0.244759
143             K00158  0.170801
144             K00159  0.193100
145             K00160  0.191877
146             K00161  0.205365
147             K00162  0.211513
148             K00163  0.210402
149             K00164  0.143525
150             K00165  0.158824
151             K00166  0.157135
152             K00167  0.082346
153             K00168  0.103279
154             K00169  0.114813
155             K00170  0.024664
156             K00171  0.084430
157             K00172  0.101503
158             K00173  0.011784
159             K00174  0.015258
160             K00175  0.018745
161             K00176  0.012090
162             K00177  0.012545
163             K00178  0.010709
164             K00179  0.208522
165             K00180  0.233894
166             K00181  0.221063
167             K00182  0.141067
168             K00183  0.170483
169             K00184  0.155907
170             K00191  0.013190
171             K00192  0.008790
172             K00193  0.012793
173             K00194  0.005873
174             K00195  0.007767
175             K00196  0.009227
176             K00209  0.006772
177             K00212  0.003517
178             K00213  0.003517
179             K00214  0.003090
180             K00215  0.003091
181             K00216  0.003272
182             K00217  0.004152
183             K00221  0.003009
184             K00222  0.003009
185             K00223  0.003009
186             K00224  0.003009
187             K00225  0.003009
188             K00226  0.003009
189             K00227  0.003009
190             K00228  0.003009
191             K00229  0.003009
192             K00230  0.003009
193             K00231  0.003009
194             K00232  0.003009
195             K00233  0.003009
196             K00234  0.003009
197             K00235  0.003009
198             K00236  0.003009
199             K00237  0.003009
200             K00238  0.003009
201             K00239  0.004630
202             K00240  0.003122
203             K00241  0.003529
204             K00242  0.004558
205             K00243  0.004254
206             K00244  0.003626
207             K00245  0.003009
208             K00246  0.003009
209             K00247  0.003009
210             K00248  0.003009
211             K00249  0.003009
212             K00250  0.003009
213             K00251  0.003009
214             K00252  0.003009
215             K00253  0.003009
216             K00254  0.003009
217             K00255  0.003009
218             K00256  0.003009
219             K00257  0.015204
220             K00258  0.041617
221             K00259  0.038843
222             K00260  0.024260
223             K00261  0.025448
224             K00262  0.027956
225             K00263  0.009633
226             K00264  0.014252
227             K00265  0.017516
228             K00266  0.007117
229             K00267  0.011013
230             K00268  0.007666
231             K00269  0.003009
232             K00270  0.003009
233             K00271  0.003009
234             K00272  0.003009
235             K00273  0.003009
236             K00274  0.003009
237             K00275  0.003009
238             K00276  0.003009
239             K00277  0.003009
240             K00278  0.003009
241             K00279  0.003009
242             K00280  0.003009
243             K00281  0.235114
244             K00282  0.259555
245             K00283  0.233654
246             K00284  0.154692
247             K00285  0.195715
248             K00286  0.186899
249             K00287  0.094677
250             K00288  0.109018
251             K00289  0.090107
252             K00290  0.023469
253             K00291  0.021600
254             K00292  0.027754
255             K00293  0.014335
256             K00294  0.010337
257             K00295  0.021833
258             K00297  0.022798
259             K00298  0.017480
260             K00299  0.035221
261             K00300  0.039400
262             K00301  0.015967
263             K00302  0.024552
264             K00303  0.028800
265             K00305  0.041010
266             K00306  0.219670
267             K00307  0.285362
268             K00308  0.231528
269             K00309  0.156597
270             K00310  0.177023
271             K00311  0.186745
272             K00312  0.202736
273             K00313  0.236045
274             K00314  0.003009
275             K00315  0.003009
276             K00316  0.003009
277             K00317  0.003009
278             K00318  0.003009
279             K00319  0.003009
280             K00321  0.003009
281             K00322  0.003009
282             K00323  0.003009
283             K00324  0.003009
284             K00325  0.003009
285             K00326  0.003009
286             K00327  0.003009
287             K00328  0.003009
288             K00329  0.003009
289             K00338  0.003009
290             K00339  0.003009
291             K00340  0.003009
292             K00341  0.003009
293             K00342  0.003009
294             K00343  0.003009
295             K00345  0.003009
296             K00354  0.003009
297             K00355  0.014006
298             K00356  0.011669
299             K00357  0.003009
300             K00358  0.011370
301             K00359  0.006031
302             K00361  0.013954
303             K00362  0.033631
304             K00363  0.045233
305             K00364  0.035195
306             K00365  0.024386
307             K00366  0.039465
308             K00367  0.039758
309             K00369  0.039873
310             K00370  0.003009
311             K00371  0.003009
312             K00372  0.003009
313             K00373  0.003009
314             K00374  0.003009
315             K00375  0.003009
316             K00376  0.003009
317             K00377  0.003009
318             K00378  0.015314
319             K00379  0.003009
320             K00380  0.003009
321             K00381  0.028566
322             K00382  0.003009
323             K00383  0.021600
324             K00384  0.029932
325             K00385  0.003009
326             K00386  0.012377
327             K00387  0.013938
328             K00388  0.003009
329             K00389  0.003009
330             K00390  0.003009
331             K00391  0.003009
332             K00392  0.003009
333             K00393  0.003009
334             K00394  0.056760
335             K00395  0.072951
336             K00396  0.066822
337             K00397  0.042201
338             K00398  0.066536
339             K00399  0.074456
340             K00400  0.003009
341             K00401  0.003009
342             K00402  0.003009
343             K00403  0.003009
344             K00404  0.003009
345             K00405  0.003009
346             K00406  0.003009
347             K00407  0.003588
348             K00408  0.004162
349             K00409  0.003009
350             K00410  0.004489
351             K00411  0.003552
352             K00412  0.003009
353             K00413  0.003009
354             K00414  0.003009
355             K00415  0.003009
356             K00416  0.003009
357             K00417  0.003009
358             K00418  0.003009
359             K00419  0.003009
360             K00420  0.003009
361             K00421  0.003009
362             K00422  0.003009
363             K00423  0.003009
364             K00424  0.003009
365             K00425  0.003009
366             K00426  0.003009
367             K00427  0.003009
368             K00428  0.003009
369             K00429  0.003009
370             K00430  0.003009
371             K00431  0.003009
372             K00432  0.003009
373             K00433  0.003009
374             K00434  0.003009
375             K00435  0.003009
376             K00436  0.003009
377             K00437  0.003009
378             K00438  0.003009
379             K00439  0.003009
380             K00440  0.003009
381             K00441  0.003009
382             K00442  0.003009
383             K00443  0.003009
384             K00444  0.003009
385             K00445  0.003009
386             K00446  0.003009
387             K00447  0.003009
388             K00448  0.003009
389             K00449  0.003009
390             K00450  0.003009
391             K00451  0.003009
392             K00452  0.003009
393             K00453  0.003009
394             K00454  0.031972
395             K00455  0.046293
396             K00456  0.048118
397             K00457  0.027574
398             K00458  0.041490
399             K00459  0.050857
400             K00460  0.003009
401             K00461  0.003009
402             K00462  0.003009
403             K00463  0.003009
404             K00464  0.003009
405             K00465  0.003009
406             K00466  0.003009
407             K00467  0.003009
408             K00468  0.003009
409             K00469  0.003009
410             K00470  0.003009
411             K00471  0.003009
412             K00472  0.003009
413             K00473  0.003009
414             K00474  0.003009
415             K00475  0.003009
416             K00476  0.003009
417             K00477  0.003009
418             K00478  0.003009
419             K00479  0.012793
420             K00480  0.013954
421             K00481  0.006411
422             K00482  0.011662
423             K00483  0.012501
424             K00484  0.183749
425             K00485  0.200730
426             K00486  0.181226
427             K00487  0.131255
428             K00488  0.154718
429             K00489  0.137803
430             K00490  0.100991
431             K00491  0.105218
432             K00492  0.101988
433             K00493  0.083080
434             K00494  0.115885
435             K00495  0.121836
436             K00496  0.026020
437             K00497  0.089054
438             K00498  0.105900
439             K00500  0.110731
440             K00501  0.003009
441             K00502  0.003009
442             K00503  0.003009
443             K00504  0.003009
444             K00505  0.003009
445             K00506  0.003009
446             K00508  0.003009
447             K00509  0.003009
448             K00510  0.003009
449             K00511  0.003009
450             K00512  0.003009
451             K00513  0.003009
452             K00514  0.003009
453             K00516  0.003009
454             K00517  0.004952
455             K00518  0.007359
456             K00519  0.009700
457             K00520  0.008050
458             K00521  0.003745
459             K00522  0.003155
460             K00524  0.007995
461             K00525  0.159694
462             K00526  0.195744
463             K00527  0.187860
464             K00528  0.119034
465             K00529  0.152939
466             K00530  0.145067
467             K00531  0.138057
468             K00532  0.165676
469             K00533  0.074703
470             K00534  0.076538
471             K00535  0.069820
472             K00536  0.053365
473             K00537  0.064541
474             K00538  0.069492
475             K00540  0.065302
476       count_of_nan  0.264868
477  count_of_nan_zero  0.223807
478      count_of_zero  0.174835
479              label  0.003009

df_IV_001 = df_IV_001.sort_values(by='IV', ascending=False)
display(round(df_IV_001.head(25),2), round(df_IV_001.tail(25),2))
# Save df_IV_001 to a CSV file
df_IV_001.to_csv(f'{files_directory}/df_IV_001.csv', index=False)

 - Os valores de IV foram gerados com o dataframe com valores NaN preenchidos com zero. Assim, é possivel uma posterior comparação entre os valores de IV com e sem os valores NaN.

- The IV values were generated with the dataframe with NaN values filled with zero. Thus, it is possible a later comparison between the IV values with and without the NaN values.

4. Pré-processamento de Dados¶

print('Intial:',df.shape, 'Current:', df_droped_001.shape)

Intial: (18983, 546) Current: (18983, 481)

Pre Processamento de Dados, uma abordagem abrangente¶

O pré-processamento de dados é uma etapa essencial na modelagem de risco de crédito. A seleção dos métodos ótimos para essa etapa é de fundamental importancia.
O objetivo principal aqui é alcançar resultados superiores, minimizando a variância.
A utilização da técnica GridSearch (como) nesse contexto permite uma exploração sistemática de várias estratégias de imputação, procedimentos de normalização e configurações algorítmicas. Esse processo visa identificar a combinação mais adequada que maximiza o poder preditivo, mantendo a estabilidade em múltiplas métricas.
A utilização de estratégias como média, mediana, mais frequente e constante para imputação, aliada à escala por MinMaxScaler, facilita a avaliação abrangente do modelo. O processo iterativo, abrangendo vários solvers e estratégias de imputação, é executado sistematicamente para garantir uma avaliação completa dos pipelines propostos.
A agregação de resultados para acurácia, precisão, recall, pontuação F1, estatística KS, coeficiente Gini e AUC facilita o processo de tomada de decisão na adequação do pipeline de preparação de dados a tarefas específicas de modelagem de risco de crédito.
O DataFrame resultante encapsula as métricas de desempenho resumidas, oferecendo insights sobre a interação entre técnicas de preparação e configurações algorítmicas.

GridSearch Approach for Data Preparation Optimization¶

In the quest to enhance the performance of credit risk models, the careful selection of optimal data preparation methods becomes of paramount importance.
The primary objective here is to achieve superior metric outcomes while minimizing variance.
The utilization of the GridSearch (like) technique in this context enables a systematic exploration of various imputation strategies, normalization procedures, and algorithmic configurations. This process aims to identify the most suitable combination that maximizes predictive power while maintaining stability across multiple metrics. Employing strategies such as mean, median, most frequent, and constant for imputation, coupled with scaling through MinMaxScaler, facilitates comprehensive model evaluation. The iterative process, encompassing multiple solvers and imputer strategies, is executed systematically to ensure a thorough assessment of the proposed pipelines.
The aggregation of results for accuracy, precision, recall, F1-score, KS statistic, Gini coefficient, and AUC facilitates an informed decision-making process in tailoring the data preparation pipeline to specific credit risk modeling tasks.
The ensuing DataFrame encapsulates the summarized performance metrics, offering insights into the interplay between preparation techniques and algorithmic configurations.

df = pd.read_csv(f'{files_directory}/df_droped_001.csv')

# Sampling to reduce the number of rows and run faster
df = df.sample(frac=1, random_state=42)
df.head()

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler, QuantileTransformer, KBinsDiscretizer, PowerTransformer
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold
from sklearn.metrics import matthews_corrcoef, cohen_kappa_score, balanced_accuracy_score, precision_recall_curve, roc_curve, class_likelihood_ratios, det_curve, roc_auc_score
import matplotlib.pyplot as plt

start_timer()

# Define a class to impute missing values with zeros
class ZeroImputer:
    def __init__(self):
        pass

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return np.nan_to_num(X)

def get_pipelines(model):
    pipelines = list()

    # Add an imputer to handle missing values
    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler()), ('m', model)])
    pipelines.append(('norm', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('m', model)])
    pipelines.append(('std', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', QuantileTransformer(n_quantiles=100, output_distribution='normal')), ('m', model)])
    pipelines.append(('quan', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')), ('m', model)])
    pipelines.append(('kbins', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', PCA(n_components=7)), ('m', model)])
    pipelines.append(('pca', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', TruncatedSVD(n_components=7)), ('m', model)])
    pipelines.append(('svd', p))

    # Alto custo computacional, será utilizada separadamente posteriormente
    # p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
    # pipelines.append(('RFE', p))

    # p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
    # pipelines.append(('stdRFE', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('p', PowerTransformer()), ('m',model)])
    pipelines.append(('power', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler((1,2))), ('p', PowerTransformer()), ('m',model)])
    pipelines.append(('MinMaxpower', p))

    return pipelines


def evaluate_model(X, y, model):
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    scores = cross_val_score(model, X, y, scoring='balanced_accuracy', cv=cv, n_jobs=-1)
    return scores


# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)

# Define the model
model = LogisticRegression(class_weight='balanced')

# Get the modeling pipelines
pipelines = get_pipelines(model)

# Evaluate each pipeline
results, names = list(), list()
for name, pipeline in pipelines:
    # Evaluate
    scores = evaluate_model(X, y, pipeline)

    # Summarize
    print('>%s: %.3f (%.3f)' % (name, np.mean(scores), np.std(scores)))

    # Store
    results.append(scores)
    names.append(name)

# Plot the result
plt.boxplot(results, labels=names, showmeans=True)
plt.title('Model Performance Comparison')
plt.xlabel('Pipeline')
plt.ylabel('Balanced Accuracy')
plt.xticks(rotation=45)
plt.show()

end_timer()

>norm: 0.629 (0.017)
>std: 0.630 (0.015)
>quan: 0.633 (0.021)
>kbins: 0.618 (0.019)
>pca: 0.572 (0.012)
>svd: 0.562 (0.047)
>power: 0.632 (0.019)
>MinMaxpower: 0.616 (0.017)

# CORRIGIR

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler, QuantileTransformer, KBinsDiscretizer, PowerTransformer
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc
from scipy.stats import ks_2samp
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn.linear_model._sag")
warnings.filterwarnings("ignore", category=FutureWarning, message="In version 1.5 onwards, subsample=200_000 will be used by default.*")


class ZeroImputer:
    def __init__(self):
        pass

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return np.nan_to_num(X)

def get_pipelines(model, imputation_strategies):
    pipelines = list()

    for strategy in imputation_strategies:
        imputer = SimpleImputer(strategy=strategy)
        pipeline = Pipeline([
            ('imputer', imputer),
            ('s', MinMaxScaler()),
            ('m', model)
        ])
        pipelines.append((strategy, pipeline))
    # Add an imputer to handle missing values
    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler()), ('m', model)])
    pipelines.append(('norm', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('m', model)])
    pipelines.append(('std', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', QuantileTransformer(n_quantiles=100, output_distribution='normal')), ('m', model)])
    pipelines.append(('quan', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')), ('m', model)])
    pipelines.append(('kbins', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', PCA(n_components=7)), ('m', model)])
    pipelines.append(('pca', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', TruncatedSVD(n_components=7)), ('m', model)])
    pipelines.append(('svd', p))

    # p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
    # pipelines.append(('RFE', p))

    # p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
    # pipelines.append(('stdRFE', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('p', PowerTransformer()), ('m',model)])
    pipelines.append(('power', p))

    p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler((1,2))), ('p', PowerTransformer()), ('m',model)])
    pipelines.append(('MinMaxpower', p))

    return pipelines

# Modify the evaluate_model function to return all metrics
def evaluate_model(X, y, model):
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    acc_scores, prec_scores, rec_scores, f1_scores, ks_scores, gini_scores, auc_scores = [], [], [], [], [], [], []

    for train_idx, test_idx in cv.split(X, y):
        X_train, X_test = X[train_idx], X[test_idx]
        y_train, y_test = y[train_idx], y[test_idx]

        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)

        acc_scores.append(accuracy_score(y_test, y_pred))
        prec_scores.append(precision_score(y_test, y_pred, zero_division=1.0))  # Set zero_division to 1.0
        rec_scores.append(recall_score(y_test, y_pred))
        f1_scores.append(f1_score(y_test, y_pred))

        # Calculate KS statistic and Gini coefficient using the predicted probabilities
        y_prob = model.predict_proba(X_test)[:, 1]
        ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
        gini_coeff = 2 * roc_auc_score(y_test, y_prob) - 1

        ks_scores.append(ks_stat)
        gini_scores.append(gini_coeff)
        auc_scores.append(roc_auc_score(y_test, y_prob))

    return {
        'accuracy': acc_scores,
        'precision': prec_scores,
        'recall': rec_scores,
        'f1': f1_scores,
        'ks': ks_scores,
        'gini': gini_scores,
        'auc': auc_scores
    }


# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()


# Define the solvers and imputer strategies to iterate over
solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
imputer_strategies = ['mean', 'median', 'most_frequent', 'constant']

# Initialize a list to store the results for each solver and strategy
all_results = []
raw_results = []
names = []

# Iterate over each imputation strategy
for imputer_strategy in imputer_strategies:
    pipelines = get_pipelines(LogisticRegression(class_weight='balanced', max_iter=10000), [imputer_strategy])

    # Iterate over each solver and pipeline for the current imputation strategy
    for solver in solvers:
        for pipeline_strategy, pipeline in pipelines:
            # Update the imputer strategy, solver, and pipeline strategy in the result_dict
            result_dict = {
                'Imputer Strategy': imputer_strategy,
                'Solver': solver,
                'Pipeline': pipeline_strategy,
            }

            # Evaluate
            metrics = evaluate_model(X, y, pipeline)

            # Store the results for each pipeline
            result_dict['Accuracy'] = np.mean(metrics['accuracy'])
            result_dict['Precision'] = np.mean(metrics['precision'])
            result_dict['Recall'] = np.mean(metrics['recall'])
            result_dict['F1'] = np.mean(metrics['f1'])
            result_dict['KS Statistic'] = np.mean(metrics['ks'])
            result_dict['Gini Coefficient'] = np.mean(metrics['gini'])
            result_dict['AUC'] = np.mean(metrics['auc'])

            # Store the results for plotting
            all_results.append(result_dict)

            # Store the results for plotting
            raw_results.append(metrics)
            names.append(pipeline_strategy)


# Create a DataFrame with all the results
df_results = pd.DataFrame(all_results)




# Plot the result
plt.boxplot([m['accuracy'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - Accuracy')
plt.xlabel('Pipeline')
plt.ylabel('Accuracy Score')
plt.xticks(rotation=45)  # Rotate the labels for better visibility
plt.show()

plt.boxplot([m['f1'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - F1')
plt.xlabel('Pipeline')
plt.ylabel('F1 Score')
plt.xticks(rotation=45)  # Rotate the labels for better visibility
plt.show()

plt.boxplot([m['ks'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - KS')
plt.xlabel('Pipeline')
plt.ylabel('KS Score')
plt.xticks(rotation=45)  # Rotate the labels for better visibility
plt.show()

df_results.head(15)

# DATA FAKE to test
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_predict
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from scipy.stats import ks_2samp

# Função para obter pipelines com diferentes estratégias de imputação
def get_pipelines(model, imputation_strategies):
    pipelines = list()

    for strategy in imputation_strategies:
        imputer = SimpleImputer(strategy=strategy)
        pipeline = Pipeline([
            ('imputer', imputer),
            ('s', MinMaxScaler()),
            ('m', model)
        ])
        pipelines.append((strategy, pipeline))

    return pipelines

# Criar um conjunto de dados fictício
#X, y = make_classification(n_samples=2000, n_features=400, n_informative=350, n_redundant=10, random_state=42)

# Definir estratégias de imputação e solvers para iterar
imputer_strategies = ['mean', 'median', 'most_frequent', 'constant']
solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']

# Inicializar listas para armazenar resultados
all_results = []

# Iterar sobre cada estratégia de imputação
for imputer_strategy in imputer_strategies:
    pipelines = get_pipelines(LogisticRegression(max_iter=10000, solver='liblinear'), [imputer_strategy])

    # Iterar sobre cada solver e pipeline para a estratégia de imputação atual
    for solver in solvers:
        for pipeline_strategy, pipeline in pipelines:
            # Inicializar listas para armazenar resultados para cada fold
            acc_scores, prec_scores, rec_scores, f1_scores, ks_scores, gini_scores, auc_scores = [], [], [], [], [], [], []

            # Realizar a validação cruzada repetida manualmente
            cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
            for train_idx, test_idx in cv.split(X, y):
                X_train, X_test = X[train_idx], X[test_idx]
                y_train, y_test = y[train_idx], y[test_idx]

                # Treinar o modelo
                pipeline.fit(X_train, y_train)

                # Obter as previsões de probabilidade
                y_pred_prob = pipeline.predict_proba(X_test)[:, 1]

                # Calcular métricas usando a função calculate_classification_metrics
                metrics_df_example = calculate_classification_metrics_one_class(y_test, y_pred_prob, target_class=1)

                # Armazenar as métricas para cada fold
                acc_scores.append(metrics_df_example.iloc[0, 1])
                prec_scores.append(metrics_df_example.iloc[0, 2])
                rec_scores.append(metrics_df_example.iloc[0, 3])
                f1_scores.append(metrics_df_example.iloc[0, 4])
                ks_scores.append(metrics_df_example.iloc[0, 5])
                gini_scores.append(metrics_df_example.iloc[0, 6])
                auc_scores.append(metrics_df_example.iloc[0, 7])

            # Calcular as médias das métricas para todos os folds
            result_dict = {
                'Imputer Strategy': imputer_strategy,
                'Solver': solver,
                'Pipeline': pipeline_strategy,
                'Accuracy': np.mean(acc_scores),
                'Precision': np.mean(prec_scores),
                'Recall': np.mean(rec_scores),
                'F1': np.mean(f1_scores),
                'KS Statistic': np.mean(ks_scores),
                'Gini Coefficient': np.mean(gini_scores),
                'AUC': np.mean(auc_scores),
            }

            # Armazenar os resultados para cada pipeline
            all_results.append(result_dict)

# Criar um DataFrame com todos os resultados
df_results = pd.DataFrame(all_results)

# Plotar os resultados
plt.boxplot([df_results[df_results['Pipeline'] == p]['Accuracy'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - Accuracy')
plt.xlabel('Imputation Strategy')
plt.ylabel('Accuracy Score')
plt.show()

plt.boxplot([df_results[df_results['Pipeline'] == p]['F1'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - F1')
plt.xlabel('Imputation Strategy')
plt.ylabel('F1 Score')
plt.show()

plt.boxplot([df_results[df_results['Pipeline'] == p]['KS Statistic'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - KS')
plt.xlabel('Imputation Strategy')
plt.ylabel('KS Score')
plt.show()

5. Depuração de Dados (Data Cleansing)¶

Boa parte da depuração dos dados foi realizada em etapas previas, dentre elas: • Eliminação de duplicatas. • Abordagens para lidar com valores faltantes. • Tipificação apropriada das variáveis. • Eliminação de erros, por exemplo, o caso de eliminação de variáveis em possível situação de MNAR. Para evitar redundância na discussão, os tópicos supracitados não serão abordados novamente.

Data cleansing was performed in previous steps, including: • Elimination of duplicates. • Approaches to deal with missing values. • Appropriate typing of variables. • Elimination of errors, for example, the case of elimination of variables in a possible situation of MNAR. To avoid redundancy in the discussion, the aforementioned topics will not be addressed again.

6. Lidando com Classes Desbalanceadas¶

Técnicas de Reamostragem de Dados:

Superamostragem.
Subamostragem.
Reamostragem híbrida.

Além disso, é relevante utilizar Ponderação de Classes (Class Weighting) em todos os algoritmos de aprendizado de máquina.

#pip install imbalanced-learn

Collecting imbalanced-learn
  Downloading imbalanced_learn-0.11.0-py3-none-any.whl.metadata (8.3 kB)
Requirement already satisfied: numpy>=1.17.3 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.26.2)
Requirement already satisfied: scipy>=1.5.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.11.4)
Requirement already satisfied: scikit-learn>=1.0.2 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.3.2)
Requirement already satisfied: joblib>=1.1.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.3.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (3.2.0)
Downloading imbalanced_learn-0.11.0-py3-none-any.whl (235 kB)
   ---------------------------------------- 0.0/235.6 kB ? eta -:--:--
   ----- ---------------------------------- 30.7/235.6 kB 1.3 MB/s eta 0:00:01
   ---------- ---------------------------- 61.4/235.6 kB 825.8 kB/s eta 0:00:01
   --------------- ----------------------- 92.2/235.6 kB 871.5 kB/s eta 0:00:01
   ------------------- ------------------ 122.9/235.6 kB 798.9 kB/s eta 0:00:01
   ---------------------------- --------- 174.1/235.6 kB 876.1 kB/s eta 0:00:01
   --------------------------------- ---- 204.8/235.6 kB 888.4 kB/s eta 0:00:01
   -------------------------------------- 235.6/235.6 kB 847.4 kB/s eta 0:00:00
Installing collected packages: imbalanced-learn
Successfully installed imbalanced-learn-0.11.0
Note: you may need to restart the kernel to use updated packages.

'''from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import average_precision_score, accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score, cohen_kappa_score, matthews_corrcoef, auc, roc_curve, precision_recall_curve
from imblearn.over_sampling import ADASYN, SMOTE
from imblearn.under_sampling import EditedNearestNeighbours, TomekLinks, ClusterCentroids, NearMiss, RepeatedEditedNearestNeighbours
from imblearn.combine import SMOTETomek, SMOTEENN
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load dataset
# X = df.drop(columns='label')
# y = df['label']
#X = X.fillna(0)


# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()

X = np.nan_to_num(X, nan=0)

# Define a function for evaluating a given model
def evaluate_model(X_train, y_train, X_test, y_test, model, class_weight=None):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]
    accuracy = accuracy_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_pred)
    balanced_f1 = f1_score(y_test, y_pred, average='weighted')
    balanced_accuracy = balanced_accuracy_score(y_test, y_pred)
    kappa = cohen_kappa_score(y_test, y_pred)
    gini = 2 * roc_auc - 1
    mcc = matthews_corrcoef(y_test, y_pred)
    ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
    auc_prc = average_precision_score(y_test, y_prob)

    return accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

results_df = pd.DataFrame()

# Original approach
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', class_weight='balanced', max_iter=500), class_weight='balanced')
print("Original Approach:")
print("Accuracy: %.2f%%" % (accuracy * 100))
print("F1 Score: %.3f" % f1)
print("Precision: %.3f" % precision)
print("ROC AUC: %.3f" % roc_auc)
print("Balanced F1 Score: %.3f" % balanced_f1)
print("Balanced Accuracy: %.3f" % balanced_accuracy)
print("Kappa: %.3f" % kappa)
print("Gini Coefficient: %.3f" % gini)
print("MCC: %.3f" % mcc)
print("KS Statistic: %.3f" % ks_stat)
print("AUC PRC: %.3f" % auc_prc)
print()

# List of combined sampling methods
combined_methods = [
    ADASYN(),
    SMOTETomek(),
    SMOTEENN(),
    SMOTE(sampling_strategy=0.50),  # Specify a custom sampling strategy
    EditedNearestNeighbours(),
    TomekLinks(),
    ClusterCentroids(),
    NearMiss(),
    RepeatedEditedNearestNeighbours()
]

# Dataframe to store the results
results_df = pd.DataFrame(columns=['Resampling Technique', 'Class Weight', 'Accuracy', 'F1 Score', 'Precision', 'ROC AUC', 'Balanced F1', 'Balanced Accuracy', 'Kappa', 'Gini Coefficient', 'MCC', 'KS Statistic', 'AUC PRC', 'Class0 Shape', 'Class1 Shape'])

# Loop through each combined method
for method in combined_methods:
    print(f"Evaluating {method.__class__.__name__}:")

    # Apply the combined sampling method to the training data
    X_resampled, y_resampled = method.fit_resample(X_train, y_train)

    # Evaluate the model using the resampled data without class_weight
    accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=800))
    print("Without Class Weight:")
    print("Accuracy: %.2f%%" % (accuracy * 100))
    print("F1 Score: %.3f" % f1)
    print("Precision: %.3f" % precision)
    print("ROC AUC: %.3f" % roc_auc)
    print("Balanced F1 Score: %.3f" % balanced_f1)
    print("Balanced Accuracy: %.3f" % balanced_accuracy)
    print("Kappa: %.3f" % kappa)
    print("Gini Coefficient: %.3f" % gini)
    print("MCC: %.3f" % mcc)
    print("KS Statistic: %.3f" % ks_stat)
    print("AUC PRC: %.3f" % auc_prc)

    # Save the results to the dataframe
    results_df = results_df.append({
        'Resampling Technique': method.__class__.__name__,
        'Class Weight': 'Without Class Weight',
        'Accuracy': accuracy,
        'F1 Score': f1,
        'Precision': precision,
        'ROC AUC': roc_auc,
        'Balanced F1': balanced_f1,
        'Balanced Accuracy': balanced_accuracy,
        'Kappa': kappa,
        'Gini Coefficient': gini,
        'MCC': mcc,
        'KS Statistic': ks_stat,
        'AUC PRC': auc_prc,
        'Class0 Shape': X_resampled[y_resampled == 0].shape,
        'Class1 Shape': X_resampled[y_resampled == 1].shape
    }, ignore_index=True)

    # Evaluate the model using the resampled data with class_weight
    accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', class_weight='balanced', max_iter=800), class_weight='balanced')
    print("With Class Weight:")
    print("Accuracy: %.2f%%" % (accuracy * 100))
    print("F1 Score: %.3f" % f1)
    print("Precision: %.3f" % precision)
    print("ROC AUC: %.3f" % roc_auc)
    print("Balanced F1 Score: %.3f" % balanced_f1)
    print("Balanced Accuracy: %.3f" % balanced_accuracy)
    print("Kappa: %.3f" % kappa)
    print("Gini Coefficient: %.3f" % gini)
    print("MCC: %.3f" % mcc)
    print("KS Statistic: %.3f" % ks_stat)
    print("AUC PRC: %.3f" % auc_prc)

    # Save the results to the dataframe
    results_df = results_df.concat({
        'Resampling Technique': method.__class__.__name__,
        'Class Weight': 'With Class Weight',
        'Accuracy': accuracy,
        'F1 Score': f1,
        'Precision': precision,
        'ROC AUC': roc_auc,
        'Balanced F1': balanced_f1,
        'Balanced Accuracy': balanced_accuracy,
        'Kappa': kappa,
        'Gini Coefficient': gini,
        'MCC': mcc,
        'KS Statistic': ks_stat,
        'AUC PRC': auc_prc,
        'Class0 Shape': X_resampled[y_resampled == 0].shape,
        'Class1 Shape': X_resampled[y_resampled == 1].shape
    }, ignore_index=True)

    print()

# Save the results dataframe to a CSV file
results_df.to_csv(f'{files_directory}/resampling_results.csv', index=False)'''

Original Approach:
Accuracy: 58.41%
F1 Score: 0.133
Precision: 0.075
ROC AUC: 0.594
Balanced F1 Score: 0.695
Balanced Accuracy: 0.594
Kappa: 0.043
Gini Coefficient: 0.188
MCC: 0.085
KS Statistic: 0.215
AUC PRC: 0.081

Evaluating ADASYN:
Without Class Weight:
Accuracy: 60.26%
F1 Score: 0.140
Precision: 0.079
ROC AUC: 0.608
Balanced F1 Score: 0.710
Balanced Accuracy: 0.608
Kappa: 0.052
Gini Coefficient: 0.217
MCC: 0.099
KS Statistic: 0.223
AUC PRC: 0.079

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_18796\3470339331.py in ?()
    104     print("KS Statistic: %.3f" % ks_stat)
    105     print("AUC PRC: %.3f" % auc_prc)
    106 
    107     # Save the results to the dataframe
--> 108     results_df = results_df.append({
    109         'Resampling Technique': method.__class__.__name__,
    110         'Class Weight': 'Without Class Weight',
    111         'Accuracy': accuracy,

c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pandas\core\generic.py in ?(self, name)
   6200             and name not in self._accessors
   6201             and self._info_axis._can_hold_identifiers_and_holds_name(name)
   6202         ):
   6203             return self[name]
-> 6204         return object.__getattribute__(self, name)

AttributeError: 'DataFrame' object has no attribute 'append'

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
    accuracy_score,
    f1_score,
    precision_score,
    roc_auc_score,
    balanced_accuracy_score,
    cohen_kappa_score,
    matthews_corrcoef,
    average_precision_score,
)
from imblearn.over_sampling import (
    ADASYN,
    SMOTE,
)
from imblearn.under_sampling import (
    EditedNearestNeighbours,
    TomekLinks,
    ClusterCentroids,
    NearMiss,
    RepeatedEditedNearestNeighbours,
    RandomUnderSampler,
)
from imblearn.combine import SMOTETomek, SMOTEENN

# Utilizar o pacote imblearn para avaliar várias técnicas de reamostragem
# Use imblearn's to evaluate several resampling techniques

# Load dataset
# X = df.drop(columns='label')
# y = df['label']
#X = X.fillna(0)


# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()

X = np.nan_to_num(X, nan=0)

# Define a function for evaluating a given model
def evaluate_model(X_train, y_train, X_test, y_test, model, class_weight=None):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    accuracy = accuracy_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_pred)
    balanced_f1 = f1_score(y_test, y_pred, average='weighted')
    balanced_accuracy = balanced_accuracy_score(y_test, y_pred)
    kappa = cohen_kappa_score(y_test, y_pred)
    gini = 2 * roc_auc_score(y_test, y_pred) - 1
    mcc = matthews_corrcoef(y_test, y_pred)
    ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
    auc_prc = average_precision_score(y_test, y_prob)

    return accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc


# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Initialize an empty list to store the DataFrames
results_dfs = []

# Original approach without class weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
    X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight=None),
    class_weight=None
)

# Create a DataFrame for the original approach
results_df = pd.DataFrame({
    'Resampling Technique': ['Original'],
    'Class Weight': ['None'],
    'Accuracy': [accuracy],
    'F1 Score': [f1],
    'Precision': [precision],
    'ROC AUC': [roc_auc],
    'Balanced F1 Score': [balanced_f1],
    'Balanced Accuracy': [balanced_accuracy],
    'Kappa': [kappa],
    'Gini Coefficient': [gini],
    'MCC': [mcc],
    'KS Statistic': [ks_stat],
    'AUC PRC': [auc_prc],
    'Sample shape X class0': [X_train[y_train == 0].shape[0]],
    'Sample shape X class1': [X_train[y_train == 1].shape[0]],
})

# Append the DataFrame to the list
results_dfs.append(results_df)

# Original approach with class weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
    X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight='balanced'),
    class_weight='balanced'
)

# Create a DataFrame for the original approach
results_df = pd.DataFrame({
    'Resampling Technique': ['Original'],
    'Class Weight': ['balanced'],
    'Accuracy': [accuracy],
    'F1 Score': [f1],
    'Precision': [precision],
    'ROC AUC': [roc_auc],
    'Balanced F1 Score': [balanced_f1],
    'Balanced Accuracy': [balanced_accuracy],
    'Kappa': [kappa],
    'Gini Coefficient': [gini],
    'MCC': [mcc],
    'KS Statistic': [ks_stat],
    'AUC PRC': [auc_prc],
    'Sample shape X class0': [X_train[y_train == 0].shape[0]],
    'Sample shape X class1': [X_train[y_train == 1].shape[0]],
})

# Append the DataFrame to the list
results_dfs.append(results_df)

# List of combined sampling methods
combined_methods = [
    ADASYN(),
    SMOTETomek(),
    SMOTEENN(),
    SMOTE(sampling_strategy=0.90),
    EditedNearestNeighbours(),
    TomekLinks(),
    ClusterCentroids(),
    NearMiss(),
    RepeatedEditedNearestNeighbours(sampling_strategy='auto'),
    RandomUnderSampler(sampling_strategy='majority'),  # Undersampling majority class
]

# Loop through each combined method
for method in combined_methods:
    # Evaluate with class_weight=None
    print(f"Evaluating {method.__class__.__name__}... Class Weight: None")
    X_resampled, y_resampled = method.fit_resample(X_train, y_train)
    accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
        X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight=None),
        class_weight=None
    )

    results_df = pd.DataFrame({
        'Resampling Technique': [method.__class__.__name__],
        'Class Weight': [None],
        'Accuracy': [accuracy],
        'F1 Score': [f1],
        'Precision': [precision],
        'ROC AUC': [roc_auc],
        'Balanced F1 Score': [balanced_f1],
        'Balanced Accuracy': [balanced_accuracy],
        'Kappa': [kappa],
        'Gini Coefficient': [gini],
        'MCC': [mcc],
        'KS Statistic': [ks_stat],
        'AUC PRC': [auc_prc],
        'Sample shape X class0': [X_resampled[y_resampled == 0].shape[0]],
        'Sample shape X class1': [X_resampled[y_resampled == 1].shape[0]],
    })

    results_dfs.append(results_df)

    # Evaluate with class_weight='balanced'
    print(f"Evaluating {method.__class__.__name__}... Class Weight: balanced")
    X_resampled, y_resampled = method.fit_resample(X_train, y_train)
    accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
        X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight='balanced'),
        class_weight='balanced'
    )

    results_df = pd.DataFrame({
        'Resampling Technique': [method.__class__.__name__],
        'Class Weight': ['balanced'],
        'Accuracy': [accuracy],
        'F1 Score': [f1],
        'Precision': [precision],
        'ROC AUC': [roc_auc],
        'Balanced F1 Score': [balanced_f1],
        'Balanced Accuracy': [balanced_accuracy],
        'Kappa': [kappa],
        'Gini Coefficient': [gini],
        'MCC': [mcc],
        'KS Statistic': [ks_stat],
        'AUC PRC': [auc_prc],
        'Sample shape X class0': [X_resampled[y_resampled == 0].shape[0]],
        'Sample shape X class1': [X_resampled[y_resampled == 1].shape[0]],
    })

    results_dfs.append(results_df)

# Concatenate all DataFrames in the list
final_results_df = pd.concat(results_dfs, ignore_index=True)

# Display the final results DataFrame
display(round(final_results_df,2))

round(final_results_df,2).to_csv(f'{files_directory}/resampling_results.csv', index=False)
end_timer()

Evaluating ADASYN... Class Weight: None
Evaluating ADASYN... Class Weight: balanced
Evaluating SMOTETomek... Class Weight: None
Evaluating SMOTETomek... Class Weight: balanced
Evaluating SMOTEENN... Class Weight: None
Evaluating SMOTEENN... Class Weight: balanced
Evaluating SMOTE... Class Weight: None
Evaluating SMOTE... Class Weight: balanced
Evaluating EditedNearestNeighbours... Class Weight: None
Evaluating EditedNearestNeighbours... Class Weight: balanced
Evaluating TomekLinks... Class Weight: None
Evaluating TomekLinks... Class Weight: balanced
Evaluating ClusterCentroids... Class Weight: None
Evaluating ClusterCentroids... Class Weight: balanced
Evaluating NearMiss... Class Weight: None
Evaluating NearMiss... Class Weight: balanced
Evaluating RepeatedEditedNearestNeighbours... Class Weight: None
Evaluating RepeatedEditedNearestNeighbours... Class Weight: balanced
Evaluating RandomUnderSampler... Class Weight: None
Evaluating RandomUnderSampler... Class Weight: balanced

round(final_results_df,2).to_csv(f'{files_directory}/resampling_results.csv', index=False)

7. Engenharia de Variáveis (ou Engenharia de Características)¶

Observou-se que o Weight of Evidence (WOE) havia sido foi previamente aplicado. Nesse método, valores contínuos são transformados em categóricos, criando 20 categorias representando percentis de 5% cada. Assim, um novo conjunto de dados foi gerado, tratando todas as variáveis como categóricas. Essa abordagem é relevante, pois NaN, outliers e outros valores que poderiam exigir tratamento prévio são agrupados em uma única categoria, eliminando a necessidade de pré-processamentos adicionais.

# Segmentos das variaveis # Features segments

sector_columns = {
    'Bank account': [f'K{i:05d}' for i in range(1, 27)],
    'Credit Card Account': [f'K{i:05d}' for i in range(27, 84)],
    'Checking account': [f'K{i:05d}' for i in range(84, 209)],
    'Saving account': ['K00209'],
    'Pre approved credit': [f'K{i:05d}' for i in range(210, 212)],
    'Financial Instability': [f'K{i:05d}' for i in range(212, 269)],
    'Assets': [f'K{i:05d}' for i in range(269, 281)],
    'Income': [f'K{i:05d}' for i in range(281, 370)],
    'Expenses': [f'K{i:05d}' for i in range(370, 466)],
    'Insurance': [f'K{i:05d}' for i in range(466, 484)],
    'Liabilities': [f'K{i:05d}' for i in range(484, 541)]
}

# Print the generated lists for each sector
for sector, columns in sector_columns.items():
    print(f"{sector}: {columns}")

Bank account: ['K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026']
Credit Card Account: ['K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083']
Checking account: ['K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208']
Saving account: ['K00209']
Pre approved credit: ['K00210', 'K00211']
Financial Instability: ['K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268']
Assets: ['K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280']
Income: ['K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369']
Expenses: ['K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465']
Insurance: ['K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483']
Liabilities: ['K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540']

sector_columns_binned = {
    'Bank account': [f'K{i:05d}_binned' for i in range(1, 27)],
    'Credit Card Account': [f'K{i:05d}_binned' for i in range(27, 84)],
    'Checking account': [f'K{i:05d}_binned' for i in range(84, 209)],
    'Saving account': ['K00209_binned'],
    'Pre approved credit': [f'K{i:05d}_binned' for i in range(210, 212)],
    'Financial Instability': [f'K{i:05d}_binned' for i in range(212, 269)],
    'Assets': [f'K{i:05d}_binned' for i in range(269, 281)],
    'Income': [f'K{i:05d}_binned' for i in range(281, 370)],
    'Expenses': [f'K{i:05d}_binned' for i in range(370, 466)],
    'Insurance': [f'K{i:05d}_binned' for i in range(466, 484)],
    'Liabilities': [f'K{i:05d}_binned' for i in range(484, 541)]
}

# Print the generated lists for each sector
for sector, columns in sector_columns_binned.items():
    print(f"{sector}: {columns}")

Bank account: ['K00001_binned', 'K00002_binned', 'K00003_binned', 'K00004_binned', 'K00005_binned', 'K00006_binned', 'K00007_binned', 'K00008_binned', 'K00009_binned', 'K00010_binned', 'K00011_binned', 'K00012_binned', 'K00013_binned', 'K00014_binned', 'K00015_binned', 'K00016_binned', 'K00017_binned', 'K00018_binned', 'K00019_binned', 'K00020_binned', 'K00021_binned', 'K00022_binned', 'K00023_binned', 'K00024_binned', 'K00025_binned', 'K00026_binned']
Credit Card Account: ['K00027_binned', 'K00028_binned', 'K00029_binned', 'K00030_binned', 'K00031_binned', 'K00032_binned', 'K00033_binned', 'K00034_binned', 'K00035_binned', 'K00036_binned', 'K00037_binned', 'K00038_binned', 'K00039_binned', 'K00040_binned', 'K00041_binned', 'K00042_binned', 'K00043_binned', 'K00044_binned', 'K00045_binned', 'K00046_binned', 'K00047_binned', 'K00048_binned', 'K00049_binned', 'K00050_binned', 'K00051_binned', 'K00052_binned', 'K00053_binned', 'K00054_binned', 'K00055_binned', 'K00056_binned', 'K00057_binned', 'K00058_binned', 'K00059_binned', 'K00060_binned', 'K00061_binned', 'K00062_binned', 'K00063_binned', 'K00064_binned', 'K00065_binned', 'K00066_binned', 'K00067_binned', 'K00068_binned', 'K00069_binned', 'K00070_binned', 'K00071_binned', 'K00072_binned', 'K00073_binned', 'K00074_binned', 'K00075_binned', 'K00076_binned', 'K00077_binned', 'K00078_binned', 'K00079_binned', 'K00080_binned', 'K00081_binned', 'K00082_binned', 'K00083_binned']
Checking account: ['K00084_binned', 'K00085_binned', 'K00086_binned', 'K00087_binned', 'K00088_binned', 'K00089_binned', 'K00090_binned', 'K00091_binned', 'K00092_binned', 'K00093_binned', 'K00094_binned', 'K00095_binned', 'K00096_binned', 'K00097_binned', 'K00098_binned', 'K00099_binned', 'K00100_binned', 'K00101_binned', 'K00102_binned', 'K00103_binned', 'K00104_binned', 'K00105_binned', 'K00106_binned', 'K00107_binned', 'K00108_binned', 'K00109_binned', 'K00110_binned', 'K00111_binned', 'K00112_binned', 'K00113_binned', 'K00114_binned', 'K00115_binned', 'K00116_binned', 'K00117_binned', 'K00118_binned', 'K00119_binned', 'K00120_binned', 'K00121_binned', 'K00122_binned', 'K00123_binned', 'K00124_binned', 'K00125_binned', 'K00126_binned', 'K00127_binned', 'K00128_binned', 'K00129_binned', 'K00130_binned', 'K00131_binned', 'K00132_binned', 'K00133_binned', 'K00134_binned', 'K00135_binned', 'K00136_binned', 'K00137_binned', 'K00138_binned', 'K00139_binned', 'K00140_binned', 'K00141_binned', 'K00142_binned', 'K00143_binned', 'K00144_binned', 'K00145_binned', 'K00146_binned', 'K00147_binned', 'K00148_binned', 'K00149_binned', 'K00150_binned', 'K00151_binned', 'K00152_binned', 'K00153_binned', 'K00154_binned', 'K00155_binned', 'K00156_binned', 'K00157_binned', 'K00158_binned', 'K00159_binned', 'K00160_binned', 'K00161_binned', 'K00162_binned', 'K00163_binned', 'K00164_binned', 'K00165_binned', 'K00166_binned', 'K00167_binned', 'K00168_binned', 'K00169_binned', 'K00170_binned', 'K00171_binned', 'K00172_binned', 'K00173_binned', 'K00174_binned', 'K00175_binned', 'K00176_binned', 'K00177_binned', 'K00178_binned', 'K00179_binned', 'K00180_binned', 'K00181_binned', 'K00182_binned', 'K00183_binned', 'K00184_binned', 'K00185_binned', 'K00186_binned', 'K00187_binned', 'K00188_binned', 'K00189_binned', 'K00190_binned', 'K00191_binned', 'K00192_binned', 'K00193_binned', 'K00194_binned', 'K00195_binned', 'K00196_binned', 'K00197_binned', 'K00198_binned', 'K00199_binned', 'K00200_binned', 'K00201_binned', 'K00202_binned', 'K00203_binned', 'K00204_binned', 'K00205_binned', 'K00206_binned', 'K00207_binned', 'K00208_binned']
Saving account: ['K00209_binned']
Pre approved credit: ['K00210_binned', 'K00211_binned']
Financial Instability: ['K00212_binned', 'K00213_binned', 'K00214_binned', 'K00215_binned', 'K00216_binned', 'K00217_binned', 'K00218_binned', 'K00219_binned', 'K00220_binned', 'K00221_binned', 'K00222_binned', 'K00223_binned', 'K00224_binned', 'K00225_binned', 'K00226_binned', 'K00227_binned', 'K00228_binned', 'K00229_binned', 'K00230_binned', 'K00231_binned', 'K00232_binned', 'K00233_binned', 'K00234_binned', 'K00235_binned', 'K00236_binned', 'K00237_binned', 'K00238_binned', 'K00239_binned', 'K00240_binned', 'K00241_binned', 'K00242_binned', 'K00243_binned', 'K00244_binned', 'K00245_binned', 'K00246_binned', 'K00247_binned', 'K00248_binned', 'K00249_binned', 'K00250_binned', 'K00251_binned', 'K00252_binned', 'K00253_binned', 'K00254_binned', 'K00255_binned', 'K00256_binned', 'K00257_binned', 'K00258_binned', 'K00259_binned', 'K00260_binned', 'K00261_binned', 'K00262_binned', 'K00263_binned', 'K00264_binned', 'K00265_binned', 'K00266_binned', 'K00267_binned', 'K00268_binned']
Assets: ['K00269_binned', 'K00270_binned', 'K00271_binned', 'K00272_binned', 'K00273_binned', 'K00274_binned', 'K00275_binned', 'K00276_binned', 'K00277_binned', 'K00278_binned', 'K00279_binned', 'K00280_binned']
Income: ['K00281_binned', 'K00282_binned', 'K00283_binned', 'K00284_binned', 'K00285_binned', 'K00286_binned', 'K00287_binned', 'K00288_binned', 'K00289_binned', 'K00290_binned', 'K00291_binned', 'K00292_binned', 'K00293_binned', 'K00294_binned', 'K00295_binned', 'K00296_binned', 'K00297_binned', 'K00298_binned', 'K00299_binned', 'K00300_binned', 'K00301_binned', 'K00302_binned', 'K00303_binned', 'K00304_binned', 'K00305_binned', 'K00306_binned', 'K00307_binned', 'K00308_binned', 'K00309_binned', 'K00310_binned', 'K00311_binned', 'K00312_binned', 'K00313_binned', 'K00314_binned', 'K00315_binned', 'K00316_binned', 'K00317_binned', 'K00318_binned', 'K00319_binned', 'K00320_binned', 'K00321_binned', 'K00322_binned', 'K00323_binned', 'K00324_binned', 'K00325_binned', 'K00326_binned', 'K00327_binned', 'K00328_binned', 'K00329_binned', 'K00330_binned', 'K00331_binned', 'K00332_binned', 'K00333_binned', 'K00334_binned', 'K00335_binned', 'K00336_binned', 'K00337_binned', 'K00338_binned', 'K00339_binned', 'K00340_binned', 'K00341_binned', 'K00342_binned', 'K00343_binned', 'K00344_binned', 'K00345_binned', 'K00346_binned', 'K00347_binned', 'K00348_binned', 'K00349_binned', 'K00350_binned', 'K00351_binned', 'K00352_binned', 'K00353_binned', 'K00354_binned', 'K00355_binned', 'K00356_binned', 'K00357_binned', 'K00358_binned', 'K00359_binned', 'K00360_binned', 'K00361_binned', 'K00362_binned', 'K00363_binned', 'K00364_binned', 'K00365_binned', 'K00366_binned', 'K00367_binned', 'K00368_binned', 'K00369_binned']
Expenses: ['K00370_binned', 'K00371_binned', 'K00372_binned', 'K00373_binned', 'K00374_binned', 'K00375_binned', 'K00376_binned', 'K00377_binned', 'K00378_binned', 'K00379_binned', 'K00380_binned', 'K00381_binned', 'K00382_binned', 'K00383_binned', 'K00384_binned', 'K00385_binned', 'K00386_binned', 'K00387_binned', 'K00388_binned', 'K00389_binned', 'K00390_binned', 'K00391_binned', 'K00392_binned', 'K00393_binned', 'K00394_binned', 'K00395_binned', 'K00396_binned', 'K00397_binned', 'K00398_binned', 'K00399_binned', 'K00400_binned', 'K00401_binned', 'K00402_binned', 'K00403_binned', 'K00404_binned', 'K00405_binned', 'K00406_binned', 'K00407_binned', 'K00408_binned', 'K00409_binned', 'K00410_binned', 'K00411_binned', 'K00412_binned', 'K00413_binned', 'K00414_binned', 'K00415_binned', 'K00416_binned', 'K00417_binned', 'K00418_binned', 'K00419_binned', 'K00420_binned', 'K00421_binned', 'K00422_binned', 'K00423_binned', 'K00424_binned', 'K00425_binned', 'K00426_binned', 'K00427_binned', 'K00428_binned', 'K00429_binned', 'K00430_binned', 'K00431_binned', 'K00432_binned', 'K00433_binned', 'K00434_binned', 'K00435_binned', 'K00436_binned', 'K00437_binned', 'K00438_binned', 'K00439_binned', 'K00440_binned', 'K00441_binned', 'K00442_binned', 'K00443_binned', 'K00444_binned', 'K00445_binned', 'K00446_binned', 'K00447_binned', 'K00448_binned', 'K00449_binned', 'K00450_binned', 'K00451_binned', 'K00452_binned', 'K00453_binned', 'K00454_binned', 'K00455_binned', 'K00456_binned', 'K00457_binned', 'K00458_binned', 'K00459_binned', 'K00460_binned', 'K00461_binned', 'K00462_binned', 'K00463_binned', 'K00464_binned', 'K00465_binned']
Insurance: ['K00466_binned', 'K00467_binned', 'K00468_binned', 'K00469_binned', 'K00470_binned', 'K00471_binned', 'K00472_binned', 'K00473_binned', 'K00474_binned', 'K00475_binned', 'K00476_binned', 'K00477_binned', 'K00478_binned', 'K00479_binned', 'K00480_binned', 'K00481_binned', 'K00482_binned', 'K00483_binned']
Liabilities: ['K00484_binned', 'K00485_binned', 'K00486_binned', 'K00487_binned', 'K00488_binned', 'K00489_binned', 'K00490_binned', 'K00491_binned', 'K00492_binned', 'K00493_binned', 'K00494_binned', 'K00495_binned', 'K00496_binned', 'K00497_binned', 'K00498_binned', 'K00499_binned', 'K00500_binned', 'K00501_binned', 'K00502_binned', 'K00503_binned', 'K00504_binned', 'K00505_binned', 'K00506_binned', 'K00507_binned', 'K00508_binned', 'K00509_binned', 'K00510_binned', 'K00511_binned', 'K00512_binned', 'K00513_binned', 'K00514_binned', 'K00515_binned', 'K00516_binned', 'K00517_binned', 'K00518_binned', 'K00519_binned', 'K00520_binned', 'K00521_binned', 'K00522_binned', 'K00523_binned', 'K00524_binned', 'K00525_binned', 'K00526_binned', 'K00527_binned', 'K00528_binned', 'K00529_binned', 'K00530_binned', 'K00531_binned', 'K00532_binned', 'K00533_binned', 'K00534_binned', 'K00535_binned', 'K00536_binned', 'K00537_binned', 'K00538_binned', 'K00539_binned', 'K00540_binned']

# Carrega o dataframe
df = pd.read_csv(f'{files_directory}/df_droped_001.csv')
df

# Normalizar as variaveis # Normalize the variables columns[2:481]
from sklearn.preprocessing import MinMaxScaler
df_norm = df.copy()
df_norm.iloc[:, 2:481] = MinMaxScaler().fit_transform(df_norm.iloc[:, 2:481])
#df_norm = df_norm.fillna(-0.05)
df_norm

# Cria um novo DataFrame para armazenar os dados agrupados
# Create a new DataFrame to store binned data
df_bins = df_norm.copy()

start_timer()
# Divide cada coluna em 20 bins e atribui rótulos
# Split features into 20 bins manually and assign labels
num_bins = 20
bin_labels = range(1, num_bins + 1)  # Use 20 labels for 20 bins

for sector, columns in sector_columns.items():
    valid_columns = [col for col in columns if col in df_bins.columns]
    if valid_columns:
        for col in valid_columns:
            col_values = df_bins[col].to_numpy()
            bins = pd.cut(col_values, bins=num_bins, labels=bin_labels, duplicates='drop')
            df_bins[f"{col}_binned"] = bins
columns_to_drop = df_bins.columns[2:481]
df_bins = df_bins.drop(columns=columns_to_drop)

# Save the resulting DataFrame with binned columns
df_bins.to_csv("binned_data.csv", index=False)
end_timer()

Tempo de execução: 4.99585223197937 segundos

df_bins['label'] = df['label'].copy()
# For categorical columns, convert NaN to a new category ( NaN as a separate category)
categorical_columns = df_bins.select_dtypes(include='category').columns
# Setitem on a Categorical with a new category (0), set the categories first
for col in categorical_columns:
   df_bins[col] = df_bins[col].cat.add_categories([0])
df_bins[categorical_columns] = df_bins[categorical_columns].fillna(0)
df_bins

# Plot binned data for each sector

# Create a list of sector names
sectors_binned = list(sector_columns_binned.keys())

start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns_binned.items():
    valid_columns_binned = [col for col in columns if col in df_bins.columns]
    if valid_columns_binned:
        plt.figure(figsize=(30, 5))
        data_subset = df_bins[valid_columns_binned].copy()  # Include 'label' column and valid columns

        data_subset['label'] = df['label'].copy()

        melted_data = data_subset.melt(id_vars=['label'], value_vars=valid_columns_binned, var_name='Feature')

        # plt.subplot(1, 2, 1)
        # sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label - Binned')

        # Set legend location explicitly and make legend box transparent
        legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
        legend.get_frame().set_facecolor('none')

        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()

end_timer()

<Figure size 1200x800 with 0 Axes>

Tempo de execução: 67.21745252609253 segundos

# StandardScaler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler, StandardScaler

# Create a list of sector names
sectors = list(sector_columns.keys())
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
    valid_columns = [col for col in columns if col in df.columns]
    if valid_columns:
        plt.figure(figsize=(20, 4))
        data_subset = df[['label'] + valid_columns]  # Include 'label' column and valid columns

        # Apply StandardScaler scaling
        scaler = StandardScaler()
        data_scaled = scaler.fit_transform(data_subset[valid_columns])
        data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
        data_scaled_df['label'] = data_subset['label']

        melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')

        plt.subplot(1, 2, 1)
        sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (Standard Scaler)')
        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()
end_timer()

<Figure size 1200x800 with 0 Axes>

Tempo de execução: 149.65817260742188 segundos

# StandardScaler with RandomUnderSampler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from imblearn.under_sampling import RandomUnderSampler

# Create a list of sector names
sectors = list(sector_columns.keys())

# Function to perform random undersampling
def perform_random_undersampling(X, y):
    sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
    X_resampled, y_resampled = sampler.fit_resample(X, y)
    return X_resampled, y_resampled

start_timer()

# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
    valid_columns = [col for col in columns if col in df.columns]
    if valid_columns:
        plt.figure(figsize=(20, 4))
        data_subset = df[['label'] + valid_columns]  # Include 'label' column and valid columns

        # Apply StandardScaler scaling
        scaler = StandardScaler()
        data_scaled = scaler.fit_transform(data_subset[valid_columns])
        data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
        data_scaled_df['label'] = data_subset['label']

        # Perform random undersampling
        X_resampled, y_resampled = perform_random_undersampling(data_scaled_df.drop('label', axis=1), data_scaled_df['label'])

        # Combine the resampled features and labels
        data_subset_resampled = pd.DataFrame(X_resampled, columns=data_scaled_df.drop('label', axis=1).columns)
        data_subset_resampled['label'] = y_resampled

        melted_data = data_subset_resampled.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')

        plt.subplot(1, 2, 1)
        sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (Standard Scaler with Random Undersampling)')
        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()

end_timer()

<Figure size 1200x800 with 0 Axes>

Tempo de execução: 25.509060621261597 segundos

# MinMaxScaler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

# Create a list of sector names
sectors = list(sector_columns.keys())
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
    valid_columns = [col for col in columns if col in df.columns]
    if valid_columns:
        plt.figure(figsize=(20, 4))
        data_subset = df[['label'] + valid_columns]  # Include 'label' column and valid columns

        # Apply StandardScaler scaling
        scaler = MinMaxScaler()
        data_scaled = scaler.fit_transform(data_subset[valid_columns])
        data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
        data_scaled_df['label'] = data_subset['label']

        melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')

        plt.subplot(1, 2, 1)
        sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (MinMax Scaler)')
        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()
end_timer()

<Figure size 1200x800 with 0 Axes>

Tempo de execução: 259.9793314933777 segundos

# MinMaxScaler with RandomUnderSampler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from imblearn.under_sampling import RandomUnderSampler

# Create a list of sector names
sectors = list(sector_columns.keys())

# Function to perform random undersampling
def perform_random_undersampling(X, y):
    sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
    X_resampled, y_resampled = sampler.fit_resample(X, y)
    return X_resampled, y_resampled

start_timer()

# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
    valid_columns = [col for col in columns if col in df.columns]
    if valid_columns:
        plt.figure(figsize=(20, 4))
        data_subset = df[['label'] + valid_columns]  # Include 'label' column and valid columns

        # Perform random undersampling
        X_resampled, y_resampled = perform_random_undersampling(data_subset.drop('label', axis=1), data_subset['label'])

        # Combine the resampled features and labels
        data_subset_resampled = pd.DataFrame(X_resampled, columns=data_subset.drop('label', axis=1).columns)
        data_subset_resampled['label'] = y_resampled

        # Apply MinMaxScaler scaling
        scaler = MinMaxScaler()
        data_scaled = scaler.fit_transform(data_subset_resampled[valid_columns])
        data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
        data_scaled_df['label'] = data_subset_resampled['label']

        melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')

        plt.subplot(1, 2, 1)
        sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (MinMax Scaler with Random Undersampling)')
        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()

end_timer()

<Figure size 1200x800 with 0 Axes>

Tempo de execução: 26.05815315246582 segundos

# Data binner with RandomUnderSample of classes
from imblearn.under_sampling import RandomUnderSampler

# Function to perform random undersampling
def perform_random_undersampling(X, y):
    sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
    X_resampled, y_resampled = sampler.fit_resample(X, y)
    return X_resampled, y_resampled

start_timer()

# Create a list of sector names
sectors_binned = list(sector_columns_binned.keys())

# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns_binned.items():
    valid_columns_binned = [col for col in columns if col in df_bins.columns]
    if valid_columns_binned:
        plt.figure(figsize=(30, 5))
        data_subset = df_bins[valid_columns_binned].copy()  # Include 'label' column and valid columns

        data_subset['label'] = df['label'].copy()

        # Perform random undersampling
        X_resampled, y_resampled = perform_random_undersampling(data_subset.drop('label', axis=1), data_subset['label'])

        # Combine the resampled features and labels
        data_subset_resampled = pd.DataFrame(X_resampled, columns=data_subset.drop('label', axis=1).columns)
        data_subset_resampled['label'] = y_resampled

        melted_data = data_subset_resampled.melt(id_vars=['label'], value_vars=valid_columns_binned, var_name='Feature')

        plt.subplot(1, 2, 2)
        sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)

        plt.xticks(rotation=45)
        plt.xlabel('Feature')
        plt.ylabel('Value')
        plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label - Binned (with Random Undersampling)')

        # Set legend location explicitly and make legend box transparent
        legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
        legend.get_frame().set_facecolor('none')

        plt.tight_layout(rect=[0, 0, 1, 0.95])
        plt.show()

print("Original Dataset:", df_bins['label'].value_counts(), "\n", "Resampled Dataset:", y_resampled.value_counts())

end_timer()

<Figure size 1200x800 with 0 Axes>

Original Dataset: label
0    17983
1     1000
Name: count, dtype: int64 
 Resampled Dataset: label
0    1000
1    1000
Name: count, dtype: int64
Tempo de execução: 13.493772506713867 segundos

8. Seleção de Variáveis¶

df_droped_001 = pd.read_csv(f'{files_directory}/df_droped_001.csv')
df_droped_001

# Correlation Methods Comparison
start_timer()
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Adjust the threshold value as needed
threshold = 0.9

# Define a function to drop highly correlated features based on threshold and NaN values
def drop_highly_correlated_columns(df, threshold=threshold):
    dropped_features_by_method = {method: [] for method in ['pearson', 'kendall', 'spearman']}
    selected_columns_by_method = {method: [] for method in ['pearson', 'kendall', 'spearman']}

    for method in ['pearson', 'kendall', 'spearman']:
        corr_matrix = df.corr(method=method)
        columns = np.full((corr_matrix.shape[0],), True, dtype=bool)
        features_to_drop = []

        for i in range(corr_matrix.shape[0]):
            for j in range(i + 1, corr_matrix.shape[0]):
                if abs(corr_matrix.iloc[i, j]) >= threshold or np.isnan(corr_matrix.iloc[i, j]):
                    if columns[j]:
                        columns[j] = False
                        features_to_drop.append(df.columns[j])

        selected_columns = df.columns[columns]
        selected_columns_by_method[method] = selected_columns
        dropped_features_by_method[method] = features_to_drop

        print(f'{method.capitalize()}:', 'Initially was {} features, if drop features by correlation, remanin {} features'.format(df.shape[1], len(selected_columns)))
        print(f'{method.capitalize()}:', 'Columns to drop by : {}'.format(set(df.columns) - set(selected_columns)), '\n')


    return selected_columns_by_method, dropped_features_by_method

# Drop highly correlated columns
selected_columns_by_method, dropped_features_by_method = drop_highly_correlated_columns(df_droped_001.iloc[:, :])

#print(f'{method}:', 'Initially was {} features, now is {} features'.format(df_droped_001.shape[1], len(selected_columns_by_method['pearson'])))
#print(f'{method}:'Columns to drop by : {}'.format(set(df_droped_001.columns) - set(selected_columns_by_method['pearson'])))

# Print dropped features for each method
for method, dropped_features in dropped_features_by_method.items():
    print(f'\n {len(dropped_features)} Features to drop by {method.capitalize()} correlation method, considering a threshold value of {threshold}: {dropped_features}.')

# Calculate intersection and union of dropped features
intersection = set(dropped_features_by_method['pearson'])
union = set(dropped_features_by_method['pearson'])
for method in ['kendall', 'spearman']:
    intersection &= set(dropped_features_by_method[method])
    union |= set(dropped_features_by_method[method])

print(f'\nIntersection features to drop: {len(intersection)}: {intersection}')
print(f'Union features to drop: {len(union)}: {union}')

# Calculate correlation matrices for different methods
corr_matrix = df.corr(method=method)
plt.figure(figsize=(20, 6))
for idx, method in enumerate(['pearson', 'kendall', 'spearman']):
    np.fill_diagonal(corr_matrix.values, 0)
    plt.subplot(1, 3, idx + 1)  # 1 row, 3 columns, position index
    sns.heatmap(df_droped_001[selected_columns_by_method[method]].corr(method=method),
                annot=False, cmap='coolwarm', fmt=".2f")
    plt.title(f'Correlation Heatmap - {method.capitalize()}')

plt.tight_layout()
plt.show()

end_timer()

Pearson: Initially was 481 features, if drop features by correlation, remanin 200 features
Pearson: Columns to drop by : {'K00184', 'K00321', 'K00328', 'K00456', 'K00452', 'K00154', 'K00300', 'K00276', 'K00075', 'K00472', 'K00339', 'K00247', 'K00283', 'K00526', 'K00159', 'K00127', 'K00338', 'K00540', 'K00495', 'K00106', 'K00306', 'K00221', 'K00490', 'K00342', 'K00384', 'K00483', 'K00447', 'K00363', 'K00310', 'K00479', 'K00451', 'K00432', 'K00441', 'K00051', 'K00068', 'K00329', 'K00044', 'K00105', 'K00371', 'K00224', 'K00516', 'K00414', 'K00175', 'K00115', 'K00534', 'K00307', 'K00160', 'K00222', 'K00177', 'K00303', 'K00166', 'K00484', 'K00038', 'K00517', 'K00512', 'K00477', 'K00359', 'K00250', 'K00060', 'K00248', 'K00289', 'K00324', 'K00458', 'K00165', 'K00514', 'K00311', 'K00413', 'K00004', 'K00326', 'K00078', 'K00529', 'K00100', 'K00080', 'K00119', 'K00494', 'K00062', 'K00435', 'K00505', 'K00538', 'K00510', 'K00345', 'K00509', 'K00511', 'K00327', 'K00032', 'K00428', 'K00475', 'K00528', 'K00521', 'K00139', 'K00063', 'K00039', 'K00265', 'K00256', 'K00151', 'K00453', 'K00422', 'K00387', 'K00136', 'K00172', 'K00533', 'K00116', 'K00122', 'K00263', 'K00045', 'K00271', 'K00488', 'K00532', 'K00340', 'K00107', 'K00297', 'K00308', 'K00171', 'K00411', 'K00244', 'K00043', 'K00474', 'K00214', 'K00313', 'K00285', 'K00410', 'K00530', 'K00003', 'K00102', 'K00148', 'K00492', 'K00079', 'K00046', 'K00169', 'K00537', 'K00489', 'K00438', 'K00113', 'K00155', 'K00163', 'K00487', 'K00108', 'K00325', 'K00262', 'K00341', 'K00069', 'K00513', 'K00096', 'K00343', 'K00226', 'K00196', 'K00252', 'K00302', 'K00213', 'K00378', 'K00178', 'K00448', 'K00120', 'K00103', 'K00424', 'K00264', 'K00468', 'K00295', 'K00081', 'K00238', 'K00425', 'K00524', 'K00404', 'K00497', 'K00042', 'K00183', 'K00405', 'K00408', 'K00033', 'K00225', 'K00358', 'K00193', 'K00519', 'K00057', 'K00132', 'K00531', 'K00427', 'K00375', 'K00459', 'K00117', 'K00322', 'K00099', 'K00056', 'K00074', 'K00110', 'K00098', 'K00372', 'K00440', 'K00124', 'K00126', 'K00168', 'K00261', 'K00104', 'K00130', 'K00461', 'K00493', 'K00138', 'K00402', 'K00536', 'K00237', 'K00522', 'K00050', 'K00121', 'K00361', 'K00268', 'K00502', 'K00241', 'K00087', 'K00305', 'K00180', 'K00366', 'K00417', 'K00508', 'K00109', 'K00217', 'K00450', 'K00374', 'K00094', 'K00429', 'K00319', 'K00157', 'K00396', 'K00015', 'K00527', 'K00255', 'K00240', 'K00114', 'K00133', 'K00399', 'K00118', 'K00292', 'count_of_zero', 'K00123', 'K00111', 'K00223', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00437', 'K00500', 'K00381', 'K00291', 'K00019', 'K00520', 'K00369', 'K00501', 'K00002', 'K00434', 'K00016', 'K00181', 'K00478', 'K00156', 'K00253', 'K00473', 'K00449', 'K00231', 'K00093', 'K00037', 'K00426', 'K00506', 'K00294', 'K00465', 'K00286', 'K00097', 'K00444', 'K00525', 'K00496', 'K00162', 'K00486', 'K00323', 'K00504', 'K00280', 'K00480', 'K00229', 'K00284', 'K00086', 'K00245', 'K00235', 'K00112'} 

Kendall: Initially was 481 features, if drop features by correlation, remanin 225 features
Kendall: Columns to drop by : {'K00321', 'K00328', 'K00137', 'K00452', 'K00154', 'K00300', 'K00276', 'K00075', 'K00472', 'K00339', 'K00379', 'K00247', 'K00398', 'K00127', 'K00338', 'K00540', 'K00495', 'K00106', 'K00380', 'K00221', 'K00482', 'K00342', 'K00483', 'K00490', 'K00447', 'K00479', 'K00423', 'K00451', 'K00476', 'K00051', 'K00068', 'K00329', 'K00441', 'K00105', 'K00272', 'K00224', 'K00464', 'K00243', 'K00516', 'K00415', 'K00301', 'K00115', 'K00484', 'K00222', 'K00303', 'K00273', 'K00215', 'K00018', 'K00517', 'K00512', 'K00477', 'K00359', 'K00250', 'K00228', 'K00445', 'K00248', 'K00071', 'K00324', 'K00458', 'K00514', 'K00153', 'K00446', 'K00392', 'K00004', 'K00216', 'K00357', 'K00326', 'K00119', 'K00494', 'K00435', 'K00505', 'K00260', 'K00518', 'K00510', 'K00345', 'K00509', 'K00511', 'K00327', 'K00032', 'K00428', 'K00463', 'K00475', 'K00521', 'K00528', 'K00139', 'K00421', 'K00265', 'K00039', 'K00416', 'K00256', 'K00453', 'K00422', 'K00387', 'K00136', 'K00172', 'K00533', 'K00116', 'K00122', 'K00532', 'K00274', 'K00317', 'K00340', 'K00152', 'K00391', 'K00107', 'K00053', 'K00297', 'K00409', 'K00411', 'K00244', 'K00278', 'K00474', 'K00214', 'K00469', 'K00313', 'K00410', 'K00003', 'K00148', 'K00169', 'K00113', 'K00325', 'K00487', 'K00108', 'K00262', 'K00341', 'K00069', 'K00513', 'K00242', 'K00267', 'K00343', 'K00226', 'K00373', 'K00196', 'K00302', 'K00178', 'K00120', 'K00448', 'K00470', 'K00424', 'K00468', 'K00295', 'K00238', 'K00425', 'K00524', 'K00404', 'K00146', 'K00497', 'K00405', 'K00225', 'K00033', 'K00358', 'K00356', 'K00193', 'K00519', 'K00057', 'K00132', 'K00531', 'K00375', 'K00293', 'K00459', 'K00117', 'K00322', 'K00110', 'K00440', 'K00124', 'K00126', 'K00318', 'K00261', 'K00104', 'K00493', 'K00138', 'K00364', 'K00536', 'K00237', 'K00147', 'K00522', 'K00121', 'K00361', 'K00268', 'K00050', 'K00502', 'K00241', 'K00305', 'K00366', 'K00417', 'K00508', 'K00194', 'K00471', 'K00279', 'K00109', 'K00450', 'K00217', 'K00481', 'K00397', 'K00374', 'K00429', 'K00249', 'K00319', 'K00015', 'K00433', 'K00255', 'K00114', 'K00133', 'K00393', 'K00399', 'K00118', 'K00292', 'K00365', 'K00111', 'K00223', 'K00123', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00131', 'K00403', 'K00500', 'K00381', 'K00291', 'K00019', 'K00266', 'K00520', 'K00006', 'K00369', 'K00457', 'K00501', 'K00002', 'K00434', 'K00016', 'K00195', 'K00112', 'K00253', 'K00449', 'K00231', 'K00426', 'K00506', 'K00145', 'K00294', 'K00465', 'K00254', 'K00525', 'K00496', 'K00386', 'K00236', 'K00323', 'K00504', 'K00280', 'K00480', 'K00229', 'K00245', 'K00427', 'K00230', 'K00439', 'K00385'} 

Spearman: Initially was 481 features, if drop features by correlation, remanin 155 features
Spearman: Columns to drop by : {'K00328', 'K00456', 'K00137', 'K00075', 'K00472', 'K00283', 'K00398', 'K00127', 'K00540', 'K00342', 'K00310', 'K00479', 'K00476', 'K00051', 'K00068', 'K00329', 'K00464', 'K00177', 'K00166', 'K00359', 'K00250', 'K00289', 'K00324', 'K00514', 'K00446', 'K00004', 'K00216', 'K00357', 'K00080', 'K00062', 'K00435', 'K00260', 'K00538', 'K00509', 'K00463', 'K00475', 'K00521', 'K00256', 'K00422', 'K00136', 'K00532', 'K00274', 'K00317', 'K00297', 'K00308', 'K00278', 'K00474', 'K00170', 'K00108', 'K00487', 'K00069', 'K00267', 'K00302', 'K00448', 'K00468', 'K00404', 'K00405', 'K00408', 'K00293', 'K00124', 'K00261', 'K00237', 'K00361', 'K00268', 'K00502', 'K00180', 'K00279', 'K00450', 'K00118', 'K00292', 'K00365', 'K00535', 'K00367', 'K00131', 'K00061', 'K00381', 'K00019', 'K00006', 'K00369', 'K00181', 'K00449', 'K00037', 'K00035', 'K00236', 'K00480', 'K00229', 'K00245', 'K00427', 'K00321', 'K00452', 'K00300', 'K00379', 'K00338', 'K00495', 'K00482', 'K00441', 'K00105', 'K00224', 'K00243', 'K00516', 'K00175', 'K00301', 'K00222', 'K00303', 'K00018', 'K00512', 'K00060', 'K00153', 'K00392', 'K00078', 'K00529', 'K00494', 'K00518', 'K00510', 'K00345', 'K00511', 'K00327', 'K00528', 'K00139', 'K00063', 'K00265', 'K00053', 'K00171', 'K00214', 'K00410', 'K00003', 'K00102', 'K00492', 'K00537', 'K00096', 'K00196', 'K00470', 'K00295', 'K00042', 'K00033', 'K00193', 'K00519', 'K00057', 'K00132', 'K00459', 'K00056', 'K00110', 'K00493', 'K00138', 'K00364', 'K00536', 'K00147', 'K00522', 'K00241', 'K00087', 'K00305', 'K00094', 'K00249', 'K00396', 'K00527', 'K00433', 'K00393', 'K00399', 'K00111', 'K00223', 'K00125', 'K00232', 'K00291', 'K00457', 'K00501', 'K00002', 'K00434', 'K00195', 'K00253', 'K00231', 'K00286', 'K00097', 'K00496', 'K00386', 'K00504', 'K00385', 'K00184', 'K00154', 'K00276', 'K00339', 'K00380', 'K00221', 'K00483', 'K00363', 'K00415', 'K00160', 'K00484', 'K00038', 'K00477', 'K00228', 'K00445', 'K00071', 'K00458', 'K00311', 'K00299', 'K00100', 'K00119', 'K00505', 'K00428', 'count_of_nan_zero', 'K00421', 'K00387', 'K00172', 'K00340', 'K00391', 'K00107', 'K00043', 'K00469', 'K00285', 'K00530', 'K00148', 'K00489', 'K00113', 'K00163', 'K00341', 'K00343', 'K00226', 'K00373', 'K00081', 'K00238', 'K00524', 'K00146', 'K00497', 'K00225', 'K00531', 'K00117', 'K00099', 'K00074', 'K00098', 'K00440', 'K00121', 'K00417', 'K00194', 'K00109', 'K00217', 'K00429', 'K00319', 'K00255', 'K00114', 'K00176', 'K00403', 'K00520', 'K00016', 'K00077', 'K00145', 'K00254', 'K00486', 'K00439', 'K00247', 'K00159', 'K00106', 'K00490', 'K00447', 'K00423', 'K00451', 'K00174', 'K00044', 'K00272', 'K00115', 'K00273', 'K00215', 'K00517', 'K00248', 'K00165', 'K00326', 'K00032', 'K00039', 'K00416', 'K00453', 'K00533', 'K00116', 'K00122', 'K00135', 'K00488', 'K00045', 'K00152', 'K00409', 'K00411', 'K00244', 'K00313', 'K00079', 'K00169', 'K00325', 'K00262', 'K00513', 'K00242', 'K00178', 'K00120', 'K00103', 'K00424', 'K00425', 'K00183', 'K00358', 'K00356', 'K00375', 'K00322', 'K00126', 'K00318', 'K00104', 'K00050', 'K00366', 'K00508', 'K00471', 'K00481', 'K00397', 'K00374', 'K00157', 'K00015', 'K00133', 'K00123', 'K00498', 'K00503', 'K00500', 'K00266', 'K00156', 'K00093', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00162', 'K00323', 'K00280', 'K00230', 'K00112'} 


 281 Features to drop by Pearson correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00019', 'K00015', 'K00016', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00037', 'K00043', 'K00038', 'K00044', 'K00045', 'K00039', 'K00042', 'K00046', 'K00050', 'K00051', 'K00056', 'K00057', 'K00060', 'K00062', 'K00063', 'K00068', 'K00069', 'K00074', 'K00075', 'K00078', 'K00079', 'K00080', 'K00081', 'K00086', 'K00087', 'K00093', 'K00098', 'K00104', 'K00105', 'K00110', 'K00116', 'K00122', 'K00155', 'K00094', 'K00099', 'K00100', 'K00106', 'K00111', 'K00112', 'K00117', 'K00118', 'K00123', 'K00124', 'K00156', 'K00283', 'K00096', 'K00097', 'K00107', 'K00108', 'K00109', 'K00119', 'K00120', 'K00121', 'K00284', 'K00285', 'K00286', 'K00102', 'K00113', 'K00114', 'K00125', 'K00126', 'K00103', 'K00115', 'K00127', 'K00157', 'K00130', 'K00132', 'K00133', 'K00517', 'K00136', 'K00138', 'K00520', 'K00139', 'K00521', 'K00522', 'K00148', 'K00151', 'K00154', 'K00307', 'K00308', 'K00313', 'K00159', 'K00160', 'K00310', 'K00311', 'K00162', 'K00163', 'K00526', 'K00527', 'K00532', 'K00165', 'K00166', 'K00488', 'K00489', 'K00529', 'K00530', 'K00168', 'K00169', 'K00493', 'K00494', 'K00495', 'K00500', 'K00171', 'K00496', 'K00497', 'K00172', 'K00498', 'K00175', 'K00177', 'K00178', 'K00180', 'K00181', 'K00183', 'K00184', 'K00478', 'K00193', 'K00479', 'K00480', 'K00196', 'K00213', 'K00263', 'K00214', 'K00264', 'K00217', 'K00328', 'K00322', 'K00229', 'K00323', 'K00324', 'K00329', 'K00231', 'K00325', 'K00326', 'K00232', 'K00327', 'K00235', 'K00237', 'K00238', 'K00240', 'K00241', 'K00244', 'K00247', 'K00250', 'K00252', 'K00253', 'K00255', 'K00256', 'K00261', 'K00262', 'K00265', 'K00268', 'K00271', 'K00276', 'K00508', 'K00280', 'K00306', 'K00289', 'K00291', 'K00292', 'K00297', 'K00294', 'K00295', 'K00300', 'K00305', 'K00302', 'K00303', 'K00321', 'K00319', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00361', 'K00358', 'K00359', 'K00363', 'K00369', 'K00366', 'K00367', 'K00371', 'K00372', 'K00374', 'K00375', 'K00378', 'K00381', 'K00384', 'K00387', 'K00396', 'K00534', 'K00399', 'K00537', 'K00538', 'K00402', 'K00404', 'K00405', 'K00408', 'K00410', 'K00411', 'K00413', 'K00414', 'K00417', 'K00422', 'K00432', 'K00434', 'K00435', 'K00437', 'K00438', 'K00440', 'K00441', 'K00444', 'K00447', 'K00456', 'K00458', 'K00459', 'K00461', 'K00502', 'K00503', 'K00465', 'K00505', 'K00506', 'K00468', 'K00473', 'K00474', 'K00477', 'K00483', 'K00486', 'K00492', 'K00519', 'K00524', 'K00540', 'count_of_zero'].

 256 Features to drop by Kendall correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00006', 'K00019', 'K00015', 'K00016', 'K00018', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00039', 'K00050', 'K00051', 'K00053', 'K00057', 'K00068', 'K00069', 'K00071', 'K00075', 'K00104', 'K00116', 'K00105', 'K00117', 'K00106', 'K00118', 'K00107', 'K00108', 'K00109', 'K00110', 'K00122', 'K00111', 'K00123', 'K00112', 'K00124', 'K00113', 'K00125', 'K00114', 'K00126', 'K00115', 'K00127', 'K00119', 'K00120', 'K00121', 'K00131', 'K00132', 'K00133', 'K00137', 'K00517', 'K00136', 'K00138', 'K00518', 'K00139', 'K00519', 'K00524', 'K00520', 'K00521', 'K00522', 'K00146', 'K00145', 'K00147', 'K00148', 'K00152', 'K00153', 'K00154', 'K00493', 'K00169', 'K00494', 'K00495', 'K00500', 'K00496', 'K00172', 'K00497', 'K00498', 'K00178', 'K00194', 'K00481', 'K00193', 'K00195', 'K00196', 'K00479', 'K00480', 'K00482', 'K00483', 'K00215', 'K00214', 'K00216', 'K00217', 'K00328', 'K00228', 'K00230', 'K00231', 'K00322', 'K00323', 'K00325', 'K00326', 'K00229', 'K00232', 'K00324', 'K00327', 'K00329', 'K00236', 'K00237', 'K00238', 'K00242', 'K00241', 'K00243', 'K00244', 'K00247', 'K00249', 'K00250', 'K00254', 'K00253', 'K00255', 'K00256', 'K00260', 'K00261', 'K00262', 'K00266', 'K00265', 'K00267', 'K00268', 'K00272', 'K00273', 'K00274', 'K00276', 'K00278', 'K00279', 'K00280', 'K00291', 'K00293', 'K00292', 'K00294', 'K00295', 'K00297', 'K00301', 'K00300', 'K00302', 'K00305', 'K00303', 'K00313', 'K00317', 'K00318', 'K00319', 'K00321', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00357', 'K00356', 'K00358', 'K00359', 'K00361', 'K00365', 'K00364', 'K00366', 'K00369', 'K00367', 'K00373', 'K00374', 'K00375', 'K00379', 'K00380', 'K00381', 'K00385', 'K00386', 'K00387', 'K00391', 'K00392', 'K00393', 'K00397', 'K00398', 'K00399', 'K00403', 'K00404', 'K00405', 'K00409', 'K00410', 'K00411', 'K00415', 'K00416', 'K00417', 'K00421', 'K00422', 'K00423', 'K00433', 'K00434', 'K00435', 'K00439', 'K00440', 'K00441', 'K00445', 'K00446', 'K00447', 'K00457', 'K00458', 'K00459', 'K00463', 'K00464', 'K00502', 'K00505', 'K00465', 'K00503', 'K00506', 'K00508', 'K00469', 'K00468', 'K00470', 'K00471', 'K00474', 'K00476', 'K00477', 'K00532', 'K00540'].

 326 Features to drop by Spearman correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00006', 'K00019', 'K00015', 'K00016', 'K00018', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00038', 'K00037', 'K00039', 'K00035', 'K00042', 'K00044', 'K00043', 'K00045', 'K00050', 'K00051', 'K00053', 'K00056', 'K00057', 'K00060', 'K00062', 'K00061', 'K00063', 'K00068', 'K00069', 'K00071', 'K00074', 'K00075', 'K00078', 'K00080', 'K00079', 'K00081', 'K00077', 'K00087', 'K00093', 'K00098', 'K00104', 'K00105', 'K00110', 'K00116', 'K00122', 'K00094', 'K00099', 'K00100', 'K00106', 'K00111', 'K00112', 'K00117', 'K00118', 'K00123', 'K00124', 'K00096', 'K00107', 'K00108', 'K00119', 'K00097', 'K00109', 'K00120', 'K00121', 'K00285', 'K00286', 'K00102', 'K00113', 'K00114', 'K00125', 'K00126', 'K00103', 'K00115', 'K00127', 'K00283', 'K00184', 'K00160', 'K00131', 'K00132', 'K00133', 'K00135', 'K00137', 'K00138', 'K00517', 'K00520', 'K00136', 'K00139', 'K00518', 'K00521', 'K00519', 'K00522', 'K00524', 'K00146', 'K00145', 'K00147', 'K00148', 'K00152', 'K00153', 'K00154', 'K00156', 'K00157', 'K00308', 'K00313', 'K00159', 'K00310', 'K00311', 'K00162', 'K00163', 'K00165', 'K00166', 'K00488', 'K00489', 'K00530', 'K00170', 'K00493', 'K00496', 'K00169', 'K00171', 'K00172', 'K00494', 'K00495', 'K00497', 'K00498', 'K00500', 'K00174', 'K00176', 'K00175', 'K00177', 'K00178', 'K00180', 'K00181', 'K00183', 'K00194', 'K00481', 'K00193', 'K00195', 'K00196', 'K00479', 'K00482', 'K00480', 'K00483', 'K00215', 'K00214', 'K00216', 'K00217', 'K00328', 'K00228', 'K00230', 'K00231', 'K00322', 'K00323', 'K00325', 'K00326', 'K00229', 'K00232', 'K00324', 'K00327', 'K00329', 'K00236', 'K00237', 'K00238', 'K00242', 'K00241', 'K00243', 'K00244', 'K00247', 'K00249', 'K00250', 'K00254', 'K00253', 'K00255', 'K00256', 'K00260', 'K00261', 'K00262', 'K00266', 'K00265', 'K00267', 'K00268', 'K00272', 'K00273', 'K00274', 'K00276', 'K00278', 'K00279', 'K00280', 'K00289', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00297', 'K00299', 'K00301', 'K00302', 'K00300', 'K00303', 'K00305', 'K00317', 'K00318', 'K00319', 'K00321', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00357', 'K00356', 'K00358', 'K00359', 'K00361', 'K00363', 'K00365', 'K00366', 'K00364', 'K00367', 'K00369', 'K00373', 'K00374', 'K00375', 'K00379', 'K00380', 'K00381', 'K00385', 'K00386', 'K00387', 'K00391', 'K00392', 'K00393', 'K00397', 'K00396', 'K00398', 'K00399', 'K00537', 'K00538', 'K00403', 'K00404', 'K00405', 'K00409', 'K00408', 'K00410', 'K00411', 'K00415', 'K00416', 'K00417', 'K00421', 'K00422', 'K00423', 'K00433', 'K00434', 'K00435', 'K00439', 'K00440', 'K00441', 'K00445', 'K00446', 'K00447', 'K00457', 'K00456', 'K00458', 'K00459', 'K00463', 'K00464', 'K00502', 'K00505', 'K00465', 'K00503', 'K00506', 'K00508', 'K00469', 'K00468', 'K00470', 'K00471', 'K00474', 'K00476', 'K00477', 'K00486', 'K00529', 'K00492', 'K00527', 'K00532', 'K00535', 'K00540', 'count_of_nan_zero'].

Intersection features to drop: 188: {'K00321', 'K00328', 'K00154', 'K00452', 'K00300', 'K00276', 'K00075', 'K00247', 'K00339', 'K00472', 'K00127', 'K00338', 'K00540', 'K00106', 'K00495', 'K00221', 'K00490', 'K00342', 'K00483', 'K00447', 'K00479', 'K00451', 'K00441', 'K00051', 'K00068', 'K00329', 'K00105', 'K00224', 'K00516', 'K00115', 'K00484', 'K00222', 'K00303', 'K00517', 'K00512', 'K00477', 'K00248', 'K00250', 'K00359', 'K00324', 'K00458', 'K00514', 'K00004', 'K00326', 'K00119', 'K00494', 'K00435', 'K00505', 'K00510', 'K00345', 'K00032', 'K00509', 'K00511', 'K00327', 'K00428', 'K00475', 'K00528', 'K00139', 'K00521', 'K00039', 'K00265', 'K00256', 'K00453', 'K00533', 'K00387', 'K00116', 'K00122', 'K00136', 'K00172', 'K00422', 'K00532', 'K00340', 'K00107', 'K00297', 'K00411', 'K00244', 'K00474', 'K00214', 'K00313', 'K00410', 'K00003', 'K00148', 'K00169', 'K00113', 'K00325', 'K00487', 'K00108', 'K00262', 'K00341', 'K00069', 'K00513', 'K00343', 'K00226', 'K00196', 'K00302', 'K00178', 'K00120', 'K00448', 'K00424', 'K00238', 'K00295', 'K00425', 'K00468', 'K00524', 'K00497', 'K00404', 'K00225', 'K00405', 'K00033', 'K00358', 'K00519', 'K00193', 'K00057', 'K00132', 'K00531', 'K00375', 'K00459', 'K00117', 'K00322', 'K00110', 'K00126', 'K00124', 'K00440', 'K00104', 'K00261', 'K00493', 'K00138', 'K00536', 'K00237', 'K00050', 'K00522', 'K00121', 'K00361', 'K00268', 'K00502', 'K00241', 'K00305', 'K00366', 'K00417', 'K00508', 'K00109', 'K00217', 'K00450', 'K00374', 'K00429', 'K00319', 'K00015', 'K00255', 'K00114', 'K00133', 'K00399', 'K00118', 'K00292', 'K00123', 'K00223', 'K00111', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00500', 'K00381', 'K00291', 'K00019', 'K00520', 'K00369', 'K00501', 'K00002', 'K00016', 'K00434', 'K00253', 'K00449', 'K00231', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00496', 'K00323', 'K00280', 'K00504', 'K00480', 'K00229', 'K00245', 'K00427', 'K00112'}
Union features to drop: 359: {'K00328', 'K00456', 'K00137', 'K00075', 'K00472', 'K00283', 'K00398', 'K00127', 'K00540', 'K00306', 'K00342', 'K00384', 'K00310', 'K00479', 'K00476', 'K00051', 'K00068', 'K00329', 'K00464', 'K00177', 'K00166', 'K00359', 'K00250', 'K00324', 'K00289', 'K00514', 'K00446', 'K00004', 'K00216', 'K00357', 'K00080', 'K00062', 'K00435', 'K00260', 'K00538', 'K00509', 'K00475', 'K00463', 'K00521', 'K00256', 'K00422', 'K00136', 'K00263', 'K00532', 'K00274', 'K00317', 'K00297', 'K00308', 'K00278', 'K00474', 'K00438', 'K00155', 'K00487', 'K00108', 'K00170', 'K00069', 'K00267', 'K00302', 'K00448', 'K00468', 'K00404', 'K00405', 'K00408', 'K00293', 'K00124', 'K00168', 'K00261', 'K00237', 'K00361', 'K00268', 'K00502', 'K00180', 'K00279', 'K00450', 'K00118', 'K00292', 'K00365', 'K00535', 'K00367', 'K00131', 'K00381', 'K00061', 'K00019', 'K00006', 'K00369', 'K00181', 'K00449', 'K00037', 'K00035', 'K00236', 'K00480', 'K00229', 'K00245', 'K00427', 'K00235', 'K00321', 'K00452', 'K00300', 'K00379', 'K00338', 'K00495', 'K00482', 'K00441', 'K00105', 'K00224', 'K00243', 'K00516', 'K00175', 'K00301', 'K00222', 'K00303', 'K00018', 'K00512', 'K00060', 'K00153', 'K00392', 'K00078', 'K00529', 'K00494', 'K00518', 'K00510', 'K00345', 'K00511', 'K00327', 'K00528', 'K00139', 'K00063', 'K00265', 'K00151', 'K00053', 'K00171', 'K00214', 'K00410', 'K00003', 'K00102', 'K00492', 'K00046', 'K00537', 'K00096', 'K00196', 'K00252', 'K00213', 'K00378', 'K00470', 'K00295', 'K00042', 'K00033', 'K00193', 'K00519', 'K00057', 'K00132', 'K00459', 'K00056', 'K00110', 'K00493', 'K00138', 'K00364', 'K00402', 'K00536', 'K00522', 'K00147', 'K00241', 'K00087', 'K00305', 'K00094', 'K00249', 'K00396', 'K00527', 'K00433', 'K00393', 'K00399', 'K00223', 'K00111', 'K00125', 'K00232', 'K00437', 'K00291', 'K00457', 'K00501', 'K00002', 'K00434', 'K00478', 'K00195', 'K00253', 'K00231', 'K00286', 'K00097', 'K00444', 'K00496', 'K00386', 'K00504', 'K00086', 'K00385', 'K00184', 'K00154', 'K00276', 'K00339', 'K00526', 'K00380', 'K00221', 'K00483', 'K00363', 'K00432', 'K00371', 'K00415', 'K00414', 'K00160', 'K00484', 'K00038', 'K00477', 'K00228', 'K00445', 'K00071', 'K00458', 'K00311', 'K00413', 'K00299', 'K00100', 'K00119', 'K00505', 'K00428', 'count_of_nan_zero', 'K00421', 'K00387', 'K00172', 'K00340', 'K00391', 'K00107', 'K00043', 'K00469', 'K00285', 'K00530', 'K00148', 'K00489', 'K00113', 'K00163', 'K00341', 'K00343', 'K00226', 'K00373', 'K00264', 'K00081', 'K00238', 'K00524', 'K00497', 'K00146', 'K00225', 'K00531', 'K00117', 'K00099', 'K00074', 'K00098', 'K00440', 'K00461', 'K00121', 'K00417', 'K00194', 'K00109', 'K00217', 'K00429', 'K00319', 'K00255', 'K00114', 'count_of_zero', 'K00176', 'K00403', 'K00520', 'K00016', 'K00077', 'K00473', 'K00145', 'K00254', 'K00486', 'K00284', 'K00439', 'K00247', 'K00159', 'K00106', 'K00490', 'K00447', 'K00423', 'K00451', 'K00174', 'K00044', 'K00272', 'K00115', 'K00534', 'K00307', 'K00273', 'K00517', 'K00215', 'K00248', 'K00165', 'K00326', 'K00032', 'K00039', 'K00416', 'K00453', 'K00533', 'K00116', 'K00122', 'K00488', 'K00135', 'K00045', 'K00271', 'K00152', 'K00409', 'K00411', 'K00244', 'K00313', 'K00079', 'K00169', 'K00325', 'K00262', 'K00513', 'K00242', 'K00178', 'K00120', 'K00103', 'K00424', 'K00425', 'K00183', 'K00358', 'K00356', 'K00375', 'K00322', 'K00372', 'K00126', 'K00318', 'K00104', 'K00130', 'K00050', 'K00366', 'K00508', 'K00471', 'K00481', 'K00397', 'K00374', 'K00157', 'K00015', 'K00240', 'K00133', 'K00123', 'K00498', 'K00503', 'K00500', 'K00266', 'K00156', 'K00093', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00162', 'K00323', 'K00280', 'K00230', 'K00112'}

Tempo de execução: 511.1961245536804 segundos

# Calculate correlation matrices for different methods with absolute values
plt.figure(figsize=(20, 6))
for idx, method in enumerate(['pearson', 'kendall', 'spearman']):
    np.fill_diagonal(corr_matrix.values, 0)
    plt.subplot(1, 3, idx + 1)  # 1 row, 3 columns, position index
    sns.heatmap(np.abs(df_droped_001[selected_columns_by_method[method]].corr(method=method)),
                annot=False, cmap='coolwarm', fmt=".2f")
    plt.title(f'Correlation Heatmap - {method.capitalize()}')

plt.tight_layout()
plt.show()

# Create a new DataFrame removing the intersection of features by correlation
df_drop = df_droped_001.loc[:, ~df_droped_001.columns.isin(intersection)]

# Display the updated DataFrame
df_drop

###   ANOVA F-Test Feature Selection   ###
start_timer()
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from matplotlib import pyplot
import pandas as pd
import numpy as np

X = df_drop.drop(['user_id', 'bank','label'], axis=1).fillna(0)
y = df_drop['label']

# Feature Selection
def select_features(X_train, y_train, X_test):
    # configure to select all features
    fs = SelectKBest(score_func=f_classif, k='all')
    # learn relationship from training data
    fs.fit(X_train, y_train)
    # transform train input data
    X_train_fs = fs.transform(X_train)
    # transform test input data
    X_test_fs = fs.transform(X_test)
    return X_train_fs, X_test_fs, fs

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# List of feature indices to drop
#features_to_drop = [28, 32, 34, 55]

# Calculate the variance of each feature
variances = X_train.var()

# Find the indices of constant features (variance = 0)
constant_feature_indices = [idx for idx, var in enumerate(variances) if var == 0]

# Convert indices to column labels
constant_feature_labels = X_train.columns[constant_feature_indices]

# Drop the constant features from X_train and X_test
X_train = X_train.drop(columns=constant_feature_labels)
X_test = X_test.drop(columns=constant_feature_labels)

# Print the dropped feature labels
print("Dropped constant feature labels:", constant_feature_labels.tolist())

# feature selection
X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)
# what are scores for the features

sorted_fscore = fs.scores_.argsort()[::-1] # Sort feature indices by scores

# Create a dictionary to store F-score and p-value information
feature_info = {'Feature': [], 'F-Score': [], 'p-Value': []}

for i in range(len(fs.scores_)):
    feature_info['Feature'].append(X_test.columns[i])
    feature_info['F-Score'].append(fs.scores_[i])  # Keep numeric values for calculations
    feature_info['p-Value'].append(fs.pvalues_[i])  # Keep numeric values for calculations

# Create a DataFrame from the dictionary
feature_df = pd.DataFrame(feature_info)
# Sort the DataFrame by F-Score and p-value
feature_df.sort_values(by=['F-Score'], ascending=False, inplace=True)

# Filter the DataFrame to select rows where p-Value is less than 0.05
significant_features = feature_df[feature_df['p-Value'] < 0.05]

# Store the significant feature names in a list
significant_feature_names = significant_features['Feature'].tolist()

# Print the list of significant feature names
print(f"Total of {len(significant_feature_names)} significant features with p-Value < 0.05:", significant_feature_names)

# Maximum number of features to select
max = 40

# Highlight top features and significant p-values
def highlight_features(val):
    color = 'background-color: blue' if val in feature_df.head(max)['F-Score'].tolist() else ''
    return color

def highlight_p_values(val):
    color = 'background-color: blue' if val < 0.01 else ''
    return color

styled_feature_df = (
    feature_df[0:max].style
    .applymap(highlight_features, subset=['F-Score'])
    .applymap(highlight_p_values, subset=['p-Value'])
)

# Display the styled DataFrame
display(styled_feature_df)

plt.figure(figsize=(24, 6))

# Plot: Choose colors based on the highest 10 F-Score and lowest p-values
colorsf = ['blue' if i in sorted_fscore[:max] else 'white' for i in range(len(fs.scores_))]
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_/fs.scores_.max() , color=colorsf, alpha=0.8)

p_threshold = 0.05  # Set the p-value threshold

colorsp = ['red' if p < p_threshold else 'white' for p in fs.pvalues_]
pyplot.bar([i for i in range(len(fs.pvalues_))], p_threshold/(fs.pvalues_+0.25),color=colorsp, alpha=0.4)

pyplot.xlabel('Feature Index')
pyplot.ylabel('Normalized F-Score and p-Value')
pyplot.title('Feature Scores and p-Value')
pyplot.show()

print('Variaveis em vermelho são significativas com p-value < 0.05 e azul são as variaveis com maior F-Score','\nVariaveis com ambas as cores devem ser selecionadas')
print('Features with color red are significant with p-value < 0.05 and blue are the top features with highest F-Score','\nFeatures with both colors should be selected')
end_timer()

Dropped constant feature labels: []
Total of 130 significant features with p-Value < 0.05: ['count_of_nan', 'count_of_nan_zero', 'K00312', 'count_of_zero', 'K00287', 'K00491', 'K00288', 'K00492', 'K00289', 'K00102', 'K00103', 'K00101', 'K00488', 'K00489', 'K00160', 'K00159', 'K00165', 'K00166', 'K00183', 'K00311', 'K00310', 'K00282', 'K00164', 'K00158', 'K00530', 'K00184', 'K00529', 'K00182', 'K00283', 'K00027', 'K00309', 'K00013', 'K00097', 'K00096', 'K00001', 'K00014', 'K00281', 'K00307', 'K00095', 'K00157', 'K00156', 'K00308', 'K00526', 'K00286', 'K00059', 'K00180', 'K00099', 'K00083', 'K00285', 'K00093', 'K00094', 'K00537', 'K00527', 'K00100', 'K00398', 'K00162', 'K00181', 'K00538', 'K00306', 'K00065', 'K00163', 'K00284', 'K00179', 'K00161', 'K00098', 'K00155', 'K00485', 'K00077', 'K00397', 'K00486', 'K00092', 'K00074', 'K00072', 'K00080', 'K00053', 'K00028', 'K00081', 'K00385', 'K00067', 'K00078', 'K00066', 'K00048', 'K00049', 'K00061', 'K00062', 'K00131', 'K00386', 'K00380', 'K00073', 'K00171', 'K00299', 'K00063', 'K00054', 'K00060', 'K00170', 'K00457', 'K00384', 'K00071', 'K00298', 'K00137', 'K00082', 'K00535', 'K00365', 'K00290', 'K00534', 'K00058', 'K00383', 'K00382', 'K00056', 'K00379', 'K00293', 'K00260', 'K00079', 'K00377', 'K00396', 'K00076', 'K00035', 'K00395', 'K00378', 'K00041', 'K00047', 'K00007', 'K00086', 'K00376', 'K00176', 'K00085', 'K00017', 'K00147', 'K00393', 'K00087']

Variaveis em vermelho são significativas com p-value < 0.05 e azul são as variaveis com maior F-Score 
Variaveis com ambas as cores devem ser selecionadas
Features with color red are significant with p-value < 0.05 and blue are the top features with highest F-Score 
Features with both colors should be selected
Tempo de execução: 1.1061046123504639 segundos

# Cria o dataframe com as variaveis selecionadas por ANOVA
df_drop_anova = df_drop.loc[:, df_drop.columns.isin(significant_feature_names)]
df_drop_anova

###    Mutual Information feature selection for numerical input data   ###
start_timer()
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import mutual_info_classif
from matplotlib import pyplot

# Feature selection
def select_features(X_train, y_train, X_test):
	# Configure to select all features
	fs = SelectKBest(score_func=mutual_info_classif, k='all')
	# Learn relationship from training data
	fs.fit(X_train, y_train)
	# Transform train input data
	X_train_fs = fs.transform(X_train)
	# Transform test input data
	X_test_fs = fs.transform(X_test)
	return X_train_fs, X_test_fs, fs


# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# Feature Selection
X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)

'''
for i in range(len(fs.scores_)):
	print(X_test.columns[i], '- Feature Mutual Information - index %d: %f' % (i, fs.scores_[i]))
# plot the scores
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)
pyplot.show()'''

# Create a dictionary to store feature names and their scores
feature_scores_dict_mi = {'Feature': [], 'Mutual_Information_Score': []}

for i in range(len(fs.scores_)):
    feature_scores_dict_mi['Feature'].append(X_test.columns[i])
    feature_scores_dict_mi['Mutual_Information_Score'].append(round(fs.scores_[i], 3))

# Create a DataFrame from the dictionary
feature_scores_df_mi = pd.DataFrame(feature_scores_dict_mi)

# Convert Mutual Information scores column to numeric
feature_scores_df_mi['Mutual_Information_Score'] = feature_scores_df_mi['Mutual_Information_Score'].astype(float)/feature_scores_df_mi['Mutual_Information_Score'].max()

# Format Mutual Information scores column to 3 decimal places
feature_scores_df_mi['Mutual_Information_Score'] = feature_scores_df_mi['Mutual_Information_Score'].apply(lambda x: '{:.3f}'.format(x))

# Print the DataFrame
print("DataFrame of Feature Mutual Information Scores:")
display(feature_scores_df_mi.T)

# Sort the DataFrame by Mutual Information scores in descending order
sorted_feature_scores_df_mi = feature_scores_df_mi.sort_values(by='Mutual_Information_Score', ascending=False)

# Maximum number of features to select
max = 40

# Print the list of selected feature names
selected_features_mi = sorted_feature_scores_df_mi.head(max)['Feature'].tolist()
print(f"List of {len(selected_features_mi)} Selected Feature Names:")
print(selected_features_mi)

# Plot the scores
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_/fs.scores_.max())
pyplot.xlabel('Feature Index')
pyplot.ylabel('Normalized Mutual Information Score')
pyplot.title('Feature Mutual Information Scores')
pyplot.show()

end_timer()

DataFrame of Feature Mutual Information Scores:

List of 40 Selected Feature Names:
['K00157', 'K00312', 'count_of_nan_zero', 'K00100', 'K00099', 'K00160', 'K00093', 'K00156', 'K00180', 'K00530', 'K00306', 'K00309', 'K00094', 'count_of_nan', 'K00092', 'K00526', 'K00308', 'K00158', 'K00307', 'K00183', 'K00310', 'K00283', 'K00486', 'K00098', 'K00165', 'K00164', 'K00159', 'K00077', 'K00284', 'K00095', 'K00166', 'K00167', 'K00155', 'count_of_zero', 'K00282', 'K00037', 'K00485', 'K00311', 'K00287', 'K00527']

Tempo de execução: 19.445337057113647 segundos

# Gerar o dataframe com as variaveis selecionadas por MI
df_drop_anova_mi = df_drop_anova.loc[:, df_drop_anova.columns.isin(selected_features_mi)]
df_drop_anova_mi

# Cria a list com as variaveis selecionadas por ANOVA e MI
# Convert the lists to sets for intersection
significant_features_set = set(significant_feature_names)
selected_features_set = set(selected_features_mi)

# Find the intersection of the two sets
intersection_features = significant_features_set.intersection(selected_features_set)

# Convert the intersection set back to a list
intersection_features_list = list(intersection_features)

# Print the list of intersection features
print("Intersection of Significant Features and Selected Features:")
print(intersection_features_list,'\n Total of Features Selected:', len(intersection_features_list))

Intersection of Significant Features and Selected Features:
['K00527', 'count_of_nan_zero', 'K00485', 'K00283', 'count_of_zero', 'K00526', 'K00159', 'K00183', 'K00306', 'K00287', 'K00099', 'count_of_nan', 'K00310', 'K00282', 'K00098', 'K00094', 'K00308', 'K00077', 'K00156', 'K00158', 'K00093', 'K00095', 'K00530', 'K00160', 'K00307', 'K00166', 'K00164', 'K00180', 'K00092', 'K00155', 'K00165', 'K00311', 'K00486', 'K00312', 'K00100', 'K00284', 'K00157', 'K00309'] 
 Total of Features Selected: 38

# Gerar o dataframe com as variaveis selecionadas por ANOVA e MI
df_drop_anova_mi = df_drop.loc[:, df_drop.columns.isin(intersection_features_list)]
df_drop_anova_mi

# Adiciona as coluna user_id, bank, label ao dataframe
df_drop_anova_mi_complete = df_drop_anova_mi.copy()
df_drop_anova_mi_complete['user_id'] = df_drop['user_id']
df_drop_anova_mi_complete['bank'] = df_drop['bank']
df_drop_anova_mi_complete['label'] = df_drop['label']

# Crie uma lista com a ordem desejada das colunas
new_order = ['user_id', 'bank'] + [col for col in df_drop_anova_mi_complete.columns if col not in ['user_id', 'bank']]

# Reorganize as colunas de acordo com a nova ordem
df_drop_anova_mi_complete = df_drop_anova_mi_complete[new_order]
df_drop_anova_mi_complete

# Atribuindo valores X e y  (df_drop_anova_mi_complete)
X = df_drop_anova_mi_complete.fillna(0)
X = pd.get_dummies(X, columns=['bank'])
X = X.drop(['user_id'], axis=1)
X = X.drop(['label'], axis=1)
# Display the resulting dataframe X
display(X, y)
y = df_drop_anova_mi_complete['label']

0        0
1        0
2        0
3        0
4        0
        ..
18978    0
18979    0
18980    0
18981    0
18982    0
Name: label, Length: 18983, dtype: int64

#X = np.nan_to_num(X, nan=0)

###   Baseline Model com as variaveis selecionadas   ###
start_timer()
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier


# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)


# Fit the model
#model = DecisionTreeClassifier(random_state=42, class_weight='balanced')
#model = SGDClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42)
#model = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42, class_weight='balanced')
#model = RandomForestClassifier(n_estimators=1000 , random_state=42, n_jobs=16, max_depth=None, class_weight='balanced')
model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)

# Evaluate the model
yhat = model.predict(X_test)

# Performance Metrics
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)

print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)

end_timer()

Accuracy: 56.49%
F1 Score: 0.125
Precision: 0.070
ROC AUC: 0.577
Balanced F1 Score: 0.680
Balanced Accuracy: 0.577
Tempo de execução: 13.036937713623047 segundos

df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False)
df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1)
df_cleandata

###   Baseline Model com dataframe df_cleandata   ###
start_timer()
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier


# Atribuindo valores X e y
X = df_cleandata.fillna(0)
X = pd.get_dummies(X, columns=['bank'])
X = X.drop(['user_id'], axis=1)
X = X.drop(['label'], axis=1)
# Display the resulting dataframe X
display(X, y)
y = df_cleandata['label']

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)


# Fit the model
#model = DecisionTreeClassifier(random_state=42, class_weight='balanced')
#model = SGDClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42)
#model = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42, class_weight='balanced')
#model = RandomForestClassifier(n_estimators=1000 , random_state=42, n_jobs=16, max_depth=None, class_weight='balanced')
model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)

# Evaluate the model
yhat = model.predict(X_test)

# Performance Metrics
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)

print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)
end_timer()

0        0
1        0
2        0
3        0
4        0
        ..
18978    0
18979    0
18980    0
18981    0
18982    0
Name: label, Length: 18983, dtype: int64

Accuracy: 57.99%
F1 Score: 0.141
Precision: 0.079
ROC AUC: 0.615
Balanced F1 Score: 0.691
Balanced Accuracy: 0.615
Tempo de execução: 124.26049423217773 segundos

# Cria função para calcular as metricas de classificação
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score

def calculate_classification_metrics(y_true, y_pred_prob):
    # Calculate binary predictions
    y_pred = (y_pred_prob >= 0.5).astype(int)

    # Confusion Matrix
    cm = confusion_matrix(y_true, y_pred)

    # True Positives, True Negatives, False Positives, False Negatives
    TP, TN, FP, FN = cm.ravel()

    # Sensitivity (True Positive Rate)
    sensitivity = TP / (TP + FN)

    # Specificity (True Negative Rate)
    specificity = TN / (TN + FP)

    # Area Under the ROC Curve (AUC-ROC)
    fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
    auc_roc = auc(fpr, tpr)

    # Area Under the Precision-Recall Curve (AUC-PRC)
    precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
    auc_prc = auc(recall, precision)

    # Gini Coefficient
    gini = 2 * auc_roc - 1

    # Kolmogorov-Smirnov (KS) statistic
    ks = np.max(tpr - fpr)

    # Matthews Correlation Coefficient (MCC)
    mcc = matthews_corrcoef(y_true, y_pred)

    # Cohen's Kappa
    kappa = cohen_kappa_score(y_true, y_pred)

    # Accuracy
    accuracy = accuracy_score(y_true, y_pred)

    # Balanced Accuracy
    balanced_accuracy = balanced_accuracy_score(y_true, y_pred)

    # F1 Score
    f1 = f1_score(y_true, y_pred)

    # Weighted F1 Score
    f1_weighted = f1_score(y_true, y_pred, average='weighted')

    # Precision
    precision = precision_score(y_true, y_pred)

    # Recall
    recall = recall_score(y_true, y_pred)

    # Create DataFrame
    metrics_df = pd.DataFrame({
        'Class': ['Combined', 'Class 0', 'Class 1'],
        'TP': [TP, cm[0, 0], cm[1, 1]],
        'TN': [TN, cm[1, 1], cm[0, 0]],
        'FP': [FP, cm[0, 1], cm[1, 0]],
        'FN': [FN, cm[1, 0], cm[0, 1]],
        'Sensitivity_TPR': [sensitivity] * 3,
        'Specificity': [specificity] * 3,
        'AUC_ROC': [auc_roc] * 3,
        'AUC_PRC': [auc_prc] * 3,
        'Gini': [gini] * 3,
        'KS': [ks] * 3,
        'MCC': [mcc] * 3,
        'Kappa': [kappa] * 3,
        'Accuracy': [accuracy] * 3,
        'Balanced_Accuracy': [balanced_accuracy] * 3,
        'F1': [f1] * 3,
        'F1_Weighted': [f1_weighted] * 3,
        'Precision': [precision] * 3,
        'Recall': [recall] * 3
    })

    return metrics_df


metrics_df_example = calculate_classification_metrics(y_test, yhat)
display(metrics_df_example)

#  Cria função para calcular as metricas de classificação #  Only Class 1 metrics

import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score

def calculate_classification_metrics_one_class(y_true, y_pred_prob, target_class=1):
    # Calculate binary predictions
    y_pred = (y_pred_prob >= 0.5).astype(int)

    # Confusion Matrix
    cm = confusion_matrix(y_true, y_pred)

    # True Positives, True Negatives, False Positives, False Negatives
    TP, TN, FP, FN = cm.ravel()

    # Sensitivity (True Positive Rate)
    sensitivity = TP / (TP + FN)

    # Specificity (True Negative Rate)
    specificity = TN / (TN + FP)

    # Area Under the ROC Curve (AUC-ROC)
    fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
    auc_roc = auc(fpr, tpr)

    # Area Under the Precision-Recall Curve (AUC-PRC)
    precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
    auc_prc = auc(recall, precision)

    # Gini Coefficient
    gini = 2 * auc_roc - 1

    # Kolmogorov-Smirnov (KS) statistic
    ks = np.max(tpr - fpr)

    # Matthews Correlation Coefficient (MCC)
    mcc = matthews_corrcoef(y_true, y_pred)

    # Cohen's Kappa
    kappa = cohen_kappa_score(y_true, y_pred)

    # Accuracy
    accuracy = accuracy_score(y_true, y_pred)

    # Balanced Accuracy
    balanced_accuracy = balanced_accuracy_score(y_true, y_pred)

    # F1 Score
    f1 = f1_score(y_true, y_pred)

    # Weighted F1 Score
    f1_weighted = f1_score(y_true, y_pred, average='weighted')

    # Precision
    precision = precision_score(y_true, y_pred)

    # Recall
    recall = recall_score(y_true, y_pred)

    # Create DataFrame
    metrics_df = pd.DataFrame({
        'Class': [f'Class {target_class}'],
        'TP': [TP],
        'TN': [TN],
        'FP': [FP],
        'FN': [FN],
        'Sensitivity_TPR': [sensitivity],
        'Specificity': [specificity],
        'AUC_ROC': [auc_roc],
        'AUC_PRC': [auc_prc],
        'Gini': [gini],
        'KS': [ks],
        'MCC': [mcc],
        'Kappa': [kappa],
        'Accuracy': [accuracy],
        'Balanced_Accuracy': [balanced_accuracy],
        'F1': [f1],
        'F1_Weighted': [f1_weighted],
        'Precision': [precision],
        'Recall': [recall]
    })

    return metrics_df

# Exemplo de uso:
metrics_df_example = calculate_classification_metrics_one_class(y_test, yhat, target_class=1)
display(metrics_df_example)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 87
     84     return metrics_df
     86 # Exemplo de uso:
---> 87 metrics_df_example = calculate_classification_metrics_one_class(y_test, yhat, target_class=1)
     88 display(metrics_df_example)

NameError: name 'y_test' is not defined

A seleção de variaveis inicial foi realizada com base nos valores de correlação, ANOVA e MI. Apesar das métricas serem similares para um modelo de referencia, houve um ganho no tempo de execução de quase 10 vezes, de 124 segundos para 13 segundos. O que é relevante quando se trata de um modelo de produção, com mais dados disponiveis.
Dados Iniciais: Accuracy: 57.99% F1 Score: 0.141 Precision: 0.079 ROC AUC: 0.615 Balanced F1 Score: 0.691 Balanced Accuracy: 0.615
- Tempo de execução: 124 segundos

Dados com Variaveis Selecionadas: Accuracy: 56.49%,F1 Score: 0.125,Precision: 0.070, ROC AUC: 0.577, Balanced F1 Score: 0.680, Balanced Accuracy: 0.577
- Tempo de execução: 13 segundos

Accuracy: 87.25% F1 Score: 0.171 Precision: 0.130 ROC AUC: 0.579 Balanced F1 Score: 0.891 Balanced Accuracy: 0.579

DT - All variable - CW Accuracy: 86.67% F1 Score: 0.157 Precision: 0.117 ROC AUC: 0.568 Balanced F1 Score: 0.887 Balanced Accuracy: 0.568
DT - All variable - No CW Accuracy: 89.70% F1 Score: 0.097 Precision: 0.090 ROC AUC: 0.523 Balanced F1 Score: 0.901 Balanced Accuracy: 0.523
DT - 36 - CW Accuracy: 83.20% F1 Score: 0.163 Precision: 0.110 ROC AUC: 0.585 Balanced F1 Score: 0.867 Balanced Accuracy: 0.585
DT - 36 - No CW Accuracy: 90.44% F1 Score: 0.076 Precision: 0.078 ROC AUC: 0.513 Balanced F1 Score: 0.904 Balanced Accuracy: 0.513
XG - 36 - CW Accuracy: 94.63% F1 Score: 0.019 Precision: 0.250 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
XG - 36 - No CW Accuracy: 94.63% F1 Score: 0.019 Precision: 0.250 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
RF - All variable - CW Accuracy: 90.81% F1 Score: 0.134 Precision: 0.133 ROC AUC: 0.543 Balanced F1 Score: 0.908 Balanced Accuracy: 0.543
RF - All variable - No CW Accuracy: 94.50% F1 Score: 0.019 Precision: 0.154 ROC AUC: 0.503 Balanced F1 Score: 0.921 Balanced Accuracy: 0.503
XG - All variable - CW Accuracy: 94.68% F1 Score: 0.019 Precision: 0.333 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
XG - All variable - No CW Accuracy: 94.68% F1 Score: 0.019 Precision: 0.333 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504

df_cleandata[['count_of_nan', 'count_of_nan_zero', 'count_of_zero']] = df_droped_001[['count_of_nan', 'count_of_nan_zero', 'count_of_zero']]

df_cleandata

print(df_IV_001.loc[df_IV_001['Feature'] != 'label'])

               Feature        IV
102             K00117  0.293708
267             K00307  0.285362
101             K00116  0.265698
476       count_of_nan  0.264868
141             K00156  0.262254
244             K00282  0.259555
103             K00118  0.257652
140             K00155  0.245358
142             K00157  0.244759
90              K00105  0.239790
78              K00093  0.237117
273             K00313  0.236045
243             K00281  0.235114
165             K00180  0.233894
245             K00283  0.233654
268             K00308  0.231528
89              K00104  0.230779
79              K00094  0.230759
77              K00092  0.230129
107             K00122  0.228140
108             K00123  0.226998
85              K00100  0.226909
91              K00106  0.224047
477  count_of_nan_zero  0.223807
166             K00181  0.221063
266             K00306  0.219670
109             K00124  0.218431
84              K00099  0.217680
95              K00110  0.217470
83              K00098  0.216294
96              K00111  0.213073
147             K00162  0.211513
148             K00163  0.210402
97              K00112  0.209657
164             K00179  0.208522
146             K00161  0.205365
272             K00312  0.202736
425             K00485  0.200730
462             K00526  0.195744
247             K00285  0.195715
105             K00120  0.194380
144             K00159  0.193100
145             K00160  0.191877
106             K00121  0.189049
463             K00527  0.187860
248             K00286  0.186899
271             K00311  0.186745
424             K00484  0.183749
94              K00109  0.183326
426             K00486  0.181226
81              K00096  0.180354
93              K00108  0.179036
270             K00310  0.177023
82              K00097  0.176811
104             K00119  0.176604
478      count_of_zero  0.174835
80              K00095  0.171099
143             K00158  0.170801
168             K00183  0.170483
468             K00532  0.165676
92              K00107  0.165097
461             K00525  0.159694
150             K00165  0.158824
151             K00166  0.157135
269             K00309  0.156597
169             K00184  0.155907
428             K00488  0.154718
246             K00284  0.154692
465             K00529  0.152939
466             K00530  0.145067
149             K00164  0.143525
111             K00126  0.142363
167             K00182  0.141067
87              K00102  0.140002
125             K00140  0.139547
126             K00141  0.139200
99              K00114  0.138751
467             K00531  0.138057
429             K00489  0.137803
110             K00125  0.136065
31              K00042  0.135852
88              K00103  0.135174
75              K00086  0.133494
74              K00085  0.133490
34              K00045  0.133440
127             K00142  0.132486
427             K00487  0.131255
112             K00127  0.131074
76              K00087  0.130009
100             K00115  0.129169
22              K00033  0.129081
33              K00044  0.128731
25              K00036  0.128550
28              K00039  0.128030
86              K00101  0.127636
21              K00032  0.125969
27              K00038  0.125732
98              K00113  0.122427
435             K00495  0.121836
19              K00030  0.120599
464             K00528  0.119034
35              K00046  0.118222
20              K00031  0.117248
434             K00494  0.115885
26              K00037  0.115540
154             K00169  0.114813
36              K00047  0.114272
32              K00043  0.110997
439             K00500  0.110731
250             K00288  0.109018
438             K00498  0.105900
30              K00041  0.105304
29              K00040  0.105282
431             K00491  0.105218
153             K00168  0.103279
432             K00492  0.101988
157             K00172  0.101503
430             K00490  0.100991
249             K00287  0.094677
1               K00001  0.091833
4               K00004  0.091174
3               K00003  0.091075
2               K00002  0.090353
251             K00289  0.090107
437             K00497  0.089054
156             K00171  0.084430
433             K00493  0.083080
152             K00167  0.082346
470             K00534  0.076538
61              K00072  0.076059
469             K00533  0.074703
339             K00399  0.074456
335             K00395  0.072951
63              K00074  0.072305
471             K00535  0.069820
474             K00538  0.069492
64              K00075  0.068626
336             K00396  0.066822
338             K00398  0.066536
43              K00054  0.065690
475             K00540  0.065302
55              K00066  0.065106
47              K00058  0.065017
473             K00537  0.064541
62              K00073  0.062009
69              K00080  0.061969
70              K00081  0.061371
58              K00069  0.061064
57              K00068  0.061027
67              K00078  0.060682
9               K00014  0.059134
45              K00056  0.057856
8               K00013  0.057652
129             K00144  0.057567
56              K00067  0.057558
334             K00394  0.056760
10              K00015  0.054569
54              K00065  0.054384
65              K00076  0.053683
472             K00536  0.053365
53              K00064  0.053086
130             K00145  0.052723
48              K00059  0.052508
46              K00057  0.052124
11              K00016  0.051786
399             K00459  0.050857
68              K00079  0.050290
49              K00060  0.049522
73              K00084  0.048754
396             K00456  0.048118
133             K00148  0.047565
51              K00062  0.047493
118             K00133  0.046961
14              K00019  0.046770
72              K00083  0.046491
18              K00029  0.046416
395             K00455  0.046293
304             K00363  0.045233
71              K00082  0.044993
37              K00048  0.044286
40              K00051  0.043353
337             K00397  0.042201
220             K00258  0.041617
398             K00458  0.041490
265             K00305  0.041010
52              K00063  0.040768
39              K00050  0.040749
114             K00129  0.040023
309             K00369  0.039873
308             K00367  0.039758
307             K00366  0.039465
261             K00300  0.039400
221             K00259  0.038843
132             K00147  0.037724
66              K00077  0.035929
260             K00299  0.035221
24              K00035  0.035197
305             K00364  0.035195
128             K00143  0.034890
303             K00362  0.033631
115             K00130  0.033256
394             K00454  0.031972
117             K00132  0.031464
38              K00049  0.030771
324             K00384  0.029932
44              K00055  0.029135
264             K00303  0.028800
321             K00381  0.028566
7               K00007  0.028465
224             K00262  0.027956
50              K00061  0.027951
254             K00292  0.027754
23              K00034  0.027625
397             K00457  0.027574
436             K00496  0.026020
223             K00261  0.025448
155             K00170  0.024664
263             K00302  0.024552
113             K00128  0.024510
306             K00365  0.024386
222             K00260  0.024260
252             K00290  0.023469
258             K00297  0.022798
16              K00027  0.022579
257             K00295  0.021833
253             K00291  0.021600
323             K00383  0.021600
0              user_id  0.020526
116             K00131  0.018936
160             K00175  0.018745
227             K00265  0.017516
259             K00298  0.017480
135             K00150  0.016956
136             K00151  0.016593
131             K00146  0.016109
262             K00301  0.015967
318             K00378  0.015314
159             K00174  0.015258
219             K00257  0.015204
255             K00293  0.014335
226             K00264  0.014252
297             K00355  0.014006
302             K00361  0.013954
420             K00480  0.013954
327             K00387  0.013938
170             K00191  0.013190
172             K00193  0.012793
419             K00479  0.012793
162             K00177  0.012545
423             K00483  0.012501
326             K00386  0.012377
161             K00176  0.012090
121             K00136  0.011919
158             K00173  0.011784
298             K00356  0.011669
422             K00482  0.011662
300             K00358  0.011370
229             K00267  0.011013
163             K00178  0.010709
139             K00154  0.010536
256             K00294  0.010337
456             K00519  0.009700
120             K00135  0.009672
225             K00263  0.009633
175             K00196  0.009227
12              K00017  0.008913
171             K00192  0.008790
457             K00520  0.008050
460             K00524  0.007995
119             K00134  0.007983
174             K00195  0.007767
13              K00018  0.007743
230             K00268  0.007666
455             K00518  0.007359
228             K00266  0.007117
17              K00028  0.006833
176             K00209  0.006772
134             K00149  0.006490
421             K00481  0.006411
138             K00153  0.006152
301             K00359  0.006031
173             K00194  0.005873
122             K00137  0.005617
137             K00152  0.005365
454             K00517  0.004952
201             K00239  0.004630
204             K00242  0.004558
350             K00410  0.004489
205             K00243  0.004254
348             K00408  0.004162
182             K00217  0.004152
458             K00521  0.003745
206             K00244  0.003626
124             K00139  0.003591
347             K00407  0.003588
351             K00411  0.003552
203             K00241  0.003529
177             K00212  0.003517
178             K00213  0.003517
181             K00216  0.003272
123             K00138  0.003249
459             K00522  0.003155
202             K00240  0.003122
180             K00215  0.003091
179             K00214  0.003090
15              K00026  0.003090
389             K00449  0.003009
384             K00444  0.003009
442             K00503  0.003009
388             K00448  0.003009
441             K00502  0.003009
387             K00447  0.003009
386             K00446  0.003009
440             K00501  0.003009
385             K00445  0.003009
372             K00432  0.003009
383             K00443  0.003009
382             K00442  0.003009
373             K00433  0.003009
369             K00429  0.003009
381             K00441  0.003009
390             K00450  0.003009
370             K00430  0.003009
380             K00440  0.003009
371             K00431  0.003009
379             K00439  0.003009
378             K00438  0.003009
377             K00437  0.003009
376             K00436  0.003009
375             K00435  0.003009
374             K00434  0.003009
443             K00504  0.003009
449             K00511  0.003009
391             K00451  0.003009
407             K00467  0.003009
367             K00427  0.003009
418             K00478  0.003009
417             K00477  0.003009
416             K00476  0.003009
415             K00475  0.003009
414             K00474  0.003009
413             K00473  0.003009
412             K00472  0.003009
411             K00471  0.003009
410             K00470  0.003009
409             K00469  0.003009
408             K00468  0.003009
453             K00516  0.003009
452             K00514  0.003009
406             K00466  0.003009
392             K00452  0.003009
405             K00465  0.003009
404             K00464  0.003009
403             K00463  0.003009
402             K00462  0.003009
401             K00461  0.003009
400             K00460  0.003009
451             K00513  0.003009
450             K00512  0.003009
448             K00510  0.003009
447             K00509  0.003009
446             K00508  0.003009
445             K00506  0.003009
444             K00505  0.003009
393             K00453  0.003009
368             K00428  0.003009
240             K00278  0.003009
366             K00426  0.003009
281             K00322  0.003009
211             K00249  0.003009
212             K00250  0.003009
213             K00251  0.003009
214             K00252  0.003009
215             K00253  0.003009
216             K00254  0.003009
217             K00255  0.003009
218             K00256  0.003009
231             K00269  0.003009
232             K00270  0.003009
233             K00271  0.003009
234             K00272  0.003009
235             K00273  0.003009
236             K00274  0.003009
237             K00275  0.003009
238             K00276  0.003009
239             K00277  0.003009
241             K00279  0.003009
242             K00280  0.003009
274             K00314  0.003009
275             K00315  0.003009
276             K00316  0.003009
277             K00317  0.003009
278             K00318  0.003009
279             K00319  0.003009
210             K00248  0.003009
209             K00247  0.003009
208             K00246  0.003009
188             K00226  0.003009
5               K00005  0.003009
6               K00006  0.003009
41              K00052  0.003009
42              K00053  0.003009
59              K00070  0.003009
60              K00071  0.003009
183             K00221  0.003009
184             K00222  0.003009
185             K00223  0.003009
186             K00224  0.003009
187             K00225  0.003009
189             K00227  0.003009
207             K00245  0.003009
190             K00228  0.003009
191             K00229  0.003009
192             K00230  0.003009
193             K00231  0.003009
194             K00232  0.003009
195             K00233  0.003009
196             K00234  0.003009
197             K00235  0.003009
198             K00236  0.003009
199             K00237  0.003009
200             K00238  0.003009
280             K00321  0.003009
282             K00323  0.003009
365             K00425  0.003009
283             K00324  0.003009
330             K00390  0.003009
331             K00391  0.003009
332             K00392  0.003009
333             K00393  0.003009
340             K00400  0.003009
341             K00401  0.003009
342             K00402  0.003009
343             K00403  0.003009
344             K00404  0.003009
345             K00405  0.003009
346             K00406  0.003009
349             K00409  0.003009
352             K00412  0.003009
353             K00413  0.003009
354             K00414  0.003009
355             K00415  0.003009
356             K00416  0.003009
357             K00417  0.003009
358             K00418  0.003009
359             K00419  0.003009
360             K00420  0.003009
361             K00421  0.003009
362             K00422  0.003009
363             K00423  0.003009
364             K00424  0.003009
329             K00389  0.003009
328             K00388  0.003009
325             K00385  0.003009
295             K00345  0.003009
284             K00325  0.003009
285             K00326  0.003009
286             K00327  0.003009
287             K00328  0.003009
288             K00329  0.003009
289             K00338  0.003009
290             K00339  0.003009
291             K00340  0.003009
292             K00341  0.003009
293             K00342  0.003009
294             K00343  0.003009
296             K00354  0.003009
322             K00382  0.003009
299             K00357  0.003009
310             K00370  0.003009
311             K00371  0.003009
312             K00372  0.003009
313             K00373  0.003009
314             K00374  0.003009
315             K00375  0.003009
316             K00376  0.003009
317             K00377  0.003009
319             K00379  0.003009
320             K00380  0.003009

### Bi-directional Elimination
# Add features in order of df_IV_001 values, drop features according to p-values and confidence intervals
import statsmodels.api as sm
import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np
import logging

start_timer()
# Copy the dataset and fill NaN values with 0
data = df_cleandata.copy()
data = data.fillna(0)

# Separate features (X) and target (y)
X = data.drop("label", axis=1)
y = data["label"]

# Create an empty list to store selected features
selected_features = []

# Define o nível de log para ERROR para o módulo statsmodels
logging.getLogger('statsmodels').setLevel(logging.ERROR)

# Create a function to fit the model and check p-values
def fit_and_check_model(X_selected, y):
    scaler = StandardScaler()
    X_selected_scaled = scaler.fit_transform(X_selected)
    X_selected_scaled = sm.add_constant(X_selected_scaled)

    model = sm.Logit(y, X_selected_scaled)
    result = model.fit_regularized(alpha=0.6, trim_mode='size', method='l1', max_iter=100000000, disp=False, qc_tol=1e-0)

    return result

# Create a function to check if zero is in [0.025, 0.975] interval

def check_zero_interval(arr):
    quantiles = np.quantile(arr, [0.025, 0.975])
    if quantiles[0] <= 0 <= quantiles[1]:
        if quantiles[0] >= 0 >= quantiles[1]:
            return True
        return False

features = df_IV_001.loc[df_IV_001['Feature'] != 'label']

# Iterate through features in the order of df_IV_001
for feature in features['Feature']:
    # Add the current feature to the selected features list
    selected_features.append(feature)

    # Subset X with selected features
    X_selected = X[selected_features]

    # Fit and check the model
    result = fit_and_check_model(X_selected, y)

    # Check if the optimization converged
    if result.mle_retvals['converged']:
        # Check p-values of existing features and drop if p-value > 0.1
        features_dropped = True
        while features_dropped:
            features_dropped = False
            for idx, pvalue in enumerate(result.pvalues[1:]):  # Exclude the constant term
                if pvalue > 0.1:
                    print(f"Dropping feature due to high p-value: {selected_features[idx]} - p-value: {pvalue}")
                    selected_features.pop(idx)
                    X_selected = X[selected_features]
                    result = fit_and_check_model(X_selected, y)
                    features_dropped = True
                    break

        # Check if the new feature's p-value is above 0.05
        if result.pvalues[-1] > 0.05:
            print(f"Dropping newly added feature due to high p-value: {feature} - p-value: {result.pvalues[-1]}")
            selected_features.pop()  # Remove the last added feature
        else:
            # Check if zero is in [0.025, 0.975] interval
            if check_zero_interval(X_selected.iloc[:, -1]):
                print(f"Dropping newly added feature due to zero in interval: {feature}")
                selected_features.pop()  # Remove the last added feature
            else:
                print(result.aic, f"Features added: {selected_features}")
                #print(result.summary())
    else:
        print(f"Features added: {selected_features}")
        print("Model did not converge\n")

# Print the final selected features
print(result.summary())
print("Final selected features:", selected_features)
end_timer()

7677.989321143859 Features added: ['K00117']
7675.682248594794 Features added: ['K00117', 'K00307']
Dropping feature due to high p-value: K00307 - p-value: 0.11323884060670035
7672.796914255221 Features added: ['K00117', 'K00116']
7606.494237142275 Features added: ['K00117', 'K00116', 'count_of_nan']
Dropping feature due to high p-value: K00156 - p-value: 0.7491528054115237
7606.494237142275 Features added: ['K00117', 'K00116', 'count_of_nan']
Dropping feature due to high p-value: K00116 - p-value: 0.11537002762492007
Dropping feature due to high p-value: K00117 - p-value: 0.16084321382181288
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00118 - p-value: 0.21474921412838321
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00155 - p-value: 0.8625678099116458
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00157 - p-value: 0.57891894126101
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00105 - p-value: 0.27545764597370925
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00093 - p-value: 0.2009176673805344
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
7606.808480155749 Features added: ['count_of_nan', 'K00282', 'K00313']
Dropping feature due to high p-value: K00313 - p-value: 0.914869181430013
Dropping newly added feature due to high p-value: K00281 - p-value: 0.0855860067372266
Dropping feature due to high p-value: K00180 - p-value: 0.45851488773064186
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00283 - p-value: 0.7580443978239031
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00308 - p-value: 0.5766969666574042
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping newly added feature due to high p-value: K00104 - p-value: 0.07237275602672022
Dropping feature due to high p-value: K00094 - p-value: 0.4269659131549074
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
7606.0498357956985 Features added: ['count_of_nan', 'K00282', 'K00092']
Dropping feature due to high p-value: K00092 - p-value: 0.7346363702899492
7604.6886331561345 Features added: ['count_of_nan', 'K00282', 'K00122']
Dropping feature due to high p-value: K00122 - p-value: 0.13937062896296662
Dropping feature due to high p-value: K00123 - p-value: 0.1123649399063858
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00100 - p-value: 0.11343142944373355
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00106 - p-value: 0.3889876998184615
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: count_of_nan - p-value: 0.18651669679183314
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping feature due to high p-value: K00181 - p-value: 0.344891910841738
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping feature due to high p-value: K00306 - p-value: 0.30204258273080564
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping newly added feature due to high p-value: K00124 - p-value: 0.05995914363457379
7598.096561502643 Features added: ['K00282', 'count_of_nan_zero', 'K00099']
Dropping feature due to high p-value: K00099 - p-value: 0.8338138903424643
7597.665823533905 Features added: ['K00282', 'count_of_nan_zero', 'K00110']
Dropping feature due to high p-value: K00110 - p-value: 0.92495431644053
7596.894761798827 Features added: ['K00282', 'count_of_nan_zero', 'K00098']
Dropping feature due to high p-value: K00098 - p-value: 0.11642252928395672
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
Dropping feature due to high p-value: K00162 - p-value: 0.5352140060786326
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
Dropping feature due to high p-value: K00163 - p-value: 0.5473461283570313
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
7599.1911801258975 Features added: ['K00282', 'count_of_nan_zero', 'K00111', 'K00112']
Dropping feature due to high p-value: K00111 - p-value: 0.7131648746810901
Dropping feature due to high p-value: K00112 - p-value: 0.18015078593045164
Dropping newly added feature due to high p-value: K00179 - p-value: 0.0529186176529721
Dropping feature due to high p-value: K00161 - p-value: 0.3165411038586735
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00485 - p-value: 0.17275074029394388
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00526 - p-value: 0.46101459593689154
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
7590.968075225388 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00285']
Dropping feature due to high p-value: K00285 - p-value: 0.44605938292380365
Dropping feature due to high p-value: K00120 - p-value: 0.7425856242420678
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00159 - p-value: 0.673285434304763
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00160 - p-value: 0.7907985337401056
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00121 - p-value: 0.7726255988406424
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00527 - p-value: 0.4444378978926208
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00286 - p-value: 0.8546656307882725
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00311 - p-value: 0.8249855020406681
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00484 - p-value: 0.14509453522452473
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00109 - p-value: 0.8798515997895365
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00486 - p-value: 0.19244480347299675
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00096 - p-value: 0.9163783838612488
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00108 - p-value: 0.8289536295007104
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00310 - p-value: 0.905740207463549
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00097 - p-value: 0.9742002753896195
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00119 - p-value: 0.7613383206720492
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: count_of_zero - p-value: 0.5927738612722486
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00095 - p-value: 0.9658795067966314
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00158 - p-value: 0.6018322596834211
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00532 - p-value: 0.9647529584019049
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00107 - p-value: 0.45607641394042076
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00525 - p-value: 0.9349148379159548
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00165 - p-value: 0.10946626253428235
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping newly added feature due to high p-value: K00166 - p-value: 0.05991386539260798
Dropping feature due to high p-value: K00309 - p-value: 0.7062083382016027
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00183 - p-value: 0.25398979375185626
7579.339416306051 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184']
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00284 - p-value: 0.35184017698476344
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00529 - p-value: 0.2330580364605246
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00488 - p-value: 0.1369335098917621
7577.740645004133 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00530']
Dropping feature due to high p-value: K00530 - p-value: 0.1841907478323298
Dropping feature due to high p-value: K00164 - p-value: 0.18489271866386892
7579.339416306051 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184']
Dropping feature due to high p-value: K00184 - p-value: 0.46820369323189415
7575.627601367731 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126']
Dropping feature due to high p-value: K00182 - p-value: 0.6400426395007348
7575.627601367731 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126']
7574.80494377063 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00102']
Dropping feature due to high p-value: K00140 - p-value: 0.12087221567603797
7574.80494377063 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00102']
Dropping newly added feature due to high p-value: K00141 - p-value: 0.07514665395357001
Dropping feature due to high p-value: K00102 - p-value: 0.6521433116519542
7559.062692364424 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114']
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
Dropping feature due to high p-value: K00489 - p-value: 0.8259665184899095
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
7556.62815191475 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00125']
Dropping newly added feature due to high p-value: K00042 - p-value: 0.08866590900183584
Dropping feature due to high p-value: K00125 - p-value: 0.5877548799091821
Dropping newly added feature due to high p-value: K00103 - p-value: 0.08683849080838922
Dropping feature due to high p-value: K00086 - p-value: 0.519097097304089
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
Dropping feature due to high p-value: K00085 - p-value: 0.24082863403210653
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
Dropping feature due to high p-value: K00142 - p-value: 0.7179962293224719
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
Dropping feature due to high p-value: K00487 - p-value: 0.49259707808095654
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
7551.593234564308 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127']
Dropping feature due to high p-value: K00087 - p-value: 0.3766139416680305
7551.593234564308 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127']
7551.613099824102 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127', 'K00115']
Dropping feature due to high p-value: K00033 - p-value: 0.25056938876096935
7551.613099824102 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127', 'K00115']
Dropping feature due to high p-value: K00045 - p-value: 0.33111409769075206
7552.533160892499 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115', 'K00044']
Dropping feature due to high p-value: K00044 - p-value: 0.14576989630490816
Dropping feature due to high p-value: K00036 - p-value: 0.13365392906409188
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00039 - p-value: 0.17044850370632403
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00101 - p-value: 0.2896473534259769
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00032 - p-value: 0.11492250514871455
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00038 - p-value: 0.14002287117779208
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00113 - p-value: 0.2708622492831999
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00495 - p-value: 0.12360101450614636
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping newly added feature due to high p-value: K00030 - p-value: 0.0936538111331149
Dropping feature due to high p-value: K00528 - p-value: 0.6776975221276043
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00046 - p-value: 0.25622283165889737
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00031 - p-value: 0.16274221150680535
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00494 - p-value: 0.18655054700872475
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00037 - p-value: 0.11740725444710791
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping newly added feature due to high p-value: K00169 - p-value: 0.09740178853680051
Dropping feature due to high p-value: K00531 - p-value: 0.10955910342672533
7552.947380950207 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047']
Dropping newly added feature due to high p-value: K00043 - p-value: 0.07613809708307055
Dropping feature due to high p-value: K00500 - p-value: 0.15337607462342456
7552.947380950207 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047']
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping newly added feature due to high p-value: K00498 - p-value: 0.09462114312557979
Dropping feature due to high p-value: K00041 - p-value: 0.9714176071417053
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00040 - p-value: 0.1692827690635328
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00491 - p-value: 0.6347386615785753
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00168 - p-value: 0.15447083066100104
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00492 - p-value: 0.21220113439435084
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping newly added feature due to high p-value: K00172 - p-value: 0.06619800818135914
Dropping feature due to high p-value: K00490 - p-value: 0.699704047487211
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00287 - p-value: 0.7981000888363039
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00047 - p-value: 0.12640361283356671
7534.317067802609 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001']
7534.318435464542 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004']
7534.318014084953 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003']
7534.318445623482 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002']
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00497 - p-value: 0.1935129342422216
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00171 - p-value: 0.13728365128780487
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00493 - p-value: 0.3265541836860618
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00167 - p-value: 0.30405647140419745
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00534 - p-value: 0.4478796861837988
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00288 - p-value: 0.9108051076399917
Dropping newly added feature due to high p-value: K00072 - p-value: 0.09452981963714274
Dropping feature due to high p-value: K00533 - p-value: 0.848511045014456
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00399 - p-value: 0.2066876425683779
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00395 - p-value: 0.4188721270224389
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping newly added feature due to high p-value: K00074 - p-value: 0.051243163895292085
Dropping feature due to high p-value: K00535 - p-value: 0.3522700163556125
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00538 - p-value: 0.12538900044114903
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping newly added feature due to high p-value: K00075 - p-value: 0.06075993420095678
Dropping feature due to high p-value: K00396 - p-value: 0.3393320446111262
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00398 - p-value: 0.21941105183459708
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00054 - p-value: 0.5998918006719778
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00540 - p-value: 0.26250105897624976
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00066 - p-value: 0.20648362945775245
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00058 - p-value: 0.5342818397740434
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00537 - p-value: 0.11735252340909139
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00080 - p-value: 0.6188078562572181
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
7524.3419354548805 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00081']
Dropping feature due to high p-value: K00081 - p-value: 0.8299464940035459
Dropping feature due to high p-value: K00069 - p-value: 0.5523066752315777
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00068 - p-value: 0.6949789109905691
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00078 - p-value: 0.7766593296958357
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00014 - p-value: 0.22121304270603193
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00056 - p-value: 0.47928457656372303
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00013 - p-value: 0.335416319659378
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00144 - p-value: 0.15391677352213964
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00067 - p-value: 0.6560855571088609
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00394 - p-value: 0.7071237594661308
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00015 - p-value: 0.27450225683668816
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00001 - p-value: 0.5701062748373682
Dropping newly added feature due to high p-value: K00065 - p-value: 0.08967589061006827
Dropping feature due to high p-value: K00076 - p-value: 0.9877126740364282
7524.583380010659 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping newly added feature due to high p-value: K00536 - p-value: 0.09037433103924347
7522.542284854546 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064']
Dropping feature due to high p-value: K00145 - p-value: 0.6864107604726889
7522.542284854546 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064']
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00057 - p-value: 0.23004787372434532
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00016 - p-value: 0.4560465794311718
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00459 - p-value: 0.9713833865870524
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00079 - p-value: 0.4344104289230778
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00084 - p-value: 0.7528651714879757
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00456 - p-value: 0.3548483122065528
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00148 - p-value: 0.8815048361196168
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00060 - p-value: 0.2359640061338999
Dropping feature due to high p-value: K00062 - p-value: 0.11263509145121835
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00019 - p-value: 0.8609185352290248
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00083 - p-value: 0.22423711374581567
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00029 - p-value: 0.48027595402738865
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00455 - p-value: 0.12721260564535175
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00363 - p-value: 0.9758705158237464
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00082 - p-value: 0.7764861723630986
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
7499.955312420818 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00048']
Dropping feature due to high p-value: K00048 - p-value: 0.7885139792831416
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00397 - p-value: 0.3241801505687303
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00258 - p-value: 0.4806711811294754
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00458 - p-value: 0.749964849550375
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00305 - p-value: 0.3906294344993262
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00063 - p-value: 0.6715280964399029
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00051 - p-value: 0.699031434961916
7500.0078449114835 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050']
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00369 - p-value: 0.926724367016016
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00367 - p-value: 0.11052057504043
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00366 - p-value: 0.15486972207663463
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00300 - p-value: 0.20019567124471804
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00259 - p-value: 0.42642005743607725
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00147 - p-value: 0.823567702453935
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00077 - p-value: 0.33291589302741886
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00299 - p-value: 0.25115408446055276
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00035 - p-value: 0.4982167976765226
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
7494.758760508257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129', 'K00364']
Dropping feature due to high p-value: K00143 - p-value: 0.6451732204285346
7494.758760508257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129', 'K00364']
Dropping feature due to high p-value: K00364 - p-value: 0.8953162605745556
Dropping feature due to high p-value: K00362 - p-value: 0.5415026356289911
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00129 - p-value: 0.22274197324650225
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00454 - p-value: 0.28243675428535886
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00132 - p-value: 0.40039757099692097
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00050 - p-value: 0.14230094569720778
Dropping feature due to high p-value: K00049 - p-value: 0.11771182635019845
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping newly added feature due to high p-value: K00384 - p-value: 0.08882653613535794
Dropping feature due to high p-value: K00055 - p-value: 0.3521398298091427
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00303 - p-value: 0.5551833974566255
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00381 - p-value: 0.20119015277420949
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00007 - p-value: 0.15575306389717253
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00262 - p-value: 0.5269429146286725
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping newly added feature due to high p-value: K00061 - p-value: 0.088096124456529
Dropping feature due to high p-value: K00292 - p-value: 0.10291483581487419
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00034 - p-value: 0.11005345334587699
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00457 - p-value: 0.667162339984448
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00496 - p-value: 0.5440956244303219
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00261 - p-value: 0.3695460467555839
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00170 - p-value: 0.4590853391738082
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00302 - p-value: 0.7315969532868962
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
7495.349290850612 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130', 'K00128']
Dropping feature due to high p-value: K00365 - p-value: 0.6365319541676079
7495.349290850612 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130', 'K00128']
Dropping newly added feature due to high p-value: K00260 - p-value: 0.08373489217648855
Dropping feature due to high p-value: K00130 - p-value: 0.958271903898335
Dropping newly added feature due to high p-value: K00290 - p-value: 0.061125198022152695
Dropping newly added feature due to high p-value: K00297 - p-value: 0.05894112313435441
Dropping feature due to high p-value: K00027 - p-value: 0.4600908124870977
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00295 - p-value: 0.47186439118808887
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00291 - p-value: 0.14061063390541476
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping newly added feature due to high p-value: K00383 - p-value: 0.09947279325796717
Dropping feature due to high p-value: user_id - p-value: 0.9844098403442806
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00131 - p-value: 0.9452293750702622
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00265 - p-value: 0.4797298920643476
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00298 - p-value: 0.9215141341234256
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00150 - p-value: 0.6586913910015506
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00151 - p-value: 0.9779066408142667
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00146 - p-value: 0.7652154764143018
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00301 - p-value: 0.9742852473267096
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00175 - p-value: 0.9824978483562365
Dropping feature due to high p-value: K00378 - p-value: 0.4258180753768942
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00174 - p-value: 0.8922154948715607
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00257 - p-value: 0.2980252325135594
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00293 - p-value: 0.28735173371220346
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00264 - p-value: 0.40306087959836434
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00355 - p-value: 0.547283354085306
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00361 - p-value: 0.8812259486496107
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00480 - p-value: 0.290218845244012
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00387 - p-value: 0.22859339497156628
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00191 - p-value: 0.6200428798311264
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
7495.348472469716 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00193']
Dropping feature due to high p-value: K00193 - p-value: 0.24777020222244595
Dropping feature due to high p-value: K00479 - p-value: 0.5549586792792015
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00126 - p-value: 0.9032972227308326
Dropping feature due to high p-value: K00114 - p-value: 0.7372867706427424
Dropping feature due to high p-value: K00004 - p-value: 0.9879605784978006
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00483 - p-value: 0.766288923158253
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00386 - p-value: 0.1088715777891812
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00177 - p-value: 0.2559680311735032
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00136 - p-value: 0.9848847083690407
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00173 - p-value: 0.7590051943896516
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00356 - p-value: 0.8790214669779173
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00482 - p-value: 0.9734808665540835
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00267 - p-value: 0.2850361075244111
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00178 - p-value: 0.27607519859933893
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00154 - p-value: 0.195253936827269
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00294 - p-value: 0.4519478647167403
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00519 - p-value: 0.9899990730449176
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00135 - p-value: 0.9275426797744033
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00263 - p-value: 0.2989325046102379
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping newly added feature due to high p-value: K00196 - p-value: 0.06332024195223265
Dropping feature due to high p-value: K00017 - p-value: 0.4458064474197858
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00192 - p-value: 0.9072765396125244
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00127 - p-value: 0.9634303110493149
Dropping feature due to high p-value: K00520 - p-value: 0.23503047473249394
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00524 - p-value: 0.8046491154561416
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00134 - p-value: 0.8962844625199424
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00195 - p-value: 0.24884337664069323
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00018 - p-value: 0.34306129552350617
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping newly added feature due to high p-value: K00268 - p-value: 0.06354220550218886
Dropping feature due to high p-value: K00518 - p-value: 0.9690876007137234
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00266 - p-value: 0.4478346703627688
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00209 - p-value: 0.36854793644078576
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00149 - p-value: 0.7249999799278605
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00481 - p-value: 0.766114249359537
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00028 - p-value: 0.9799097986839702
Dropping feature due to high p-value: K00153 - p-value: 0.12906280799245656
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00358 - p-value: 0.6391650729952021
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
7472.752629272268 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00194']
Dropping feature due to high p-value: K00194 - p-value: 0.9966129648239309
Dropping feature due to high p-value: K00137 - p-value: 0.1756002039575536
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00152 - p-value: 0.27292150542467497
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00517 - p-value: 0.8725080464157869
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00239 - p-value: 0.5553624702671622
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00242 - p-value: 0.14993827082482455
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00410 - p-value: 0.5152370574840717
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
7470.387316805825 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243']
Dropping feature due to high p-value: K00408 - p-value: 0.8688473948759272
7470.387316805825 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243']
7465.5265238294505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243', 'K00217']
Dropping feature due to high p-value: K00521 - p-value: 0.14687857232689042
7465.5265238294505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243', 'K00217']
Dropping feature due to high p-value: K00243 - p-value: 0.7747185369822046
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00139 - p-value: 0.23629674619171992
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00407 - p-value: 0.7315039482339845
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00411 - p-value: 0.25781386847102206
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00241 - p-value: 0.9254518619375817
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00212 - p-value: 0.4170578506641157
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00213 - p-value: 0.5383731259724079
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00217 - p-value: 0.662894795770799
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00138 - p-value: 0.1420635330121469
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00522 - p-value: 0.21632005000991938
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00240 - p-value: 0.8726524265364154
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00216 - p-value: 0.36490388245286676
7465.304079628008 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215']
Dropping feature due to high p-value: K00214 - p-value: 0.36426976297681934
7465.304079628008 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215']
Dropping feature due to high p-value: K00003 - p-value: 0.5894124013727395
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00449 - p-value: 0.18477666678166238
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00444 - p-value: 0.327020100035051
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00503 - p-value: 0.7526304240222759
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00448 - p-value: 0.13689583937095923
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00502 - p-value: 0.8484100362673181
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00447 - p-value: 0.10836185962121456
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00446 - p-value: 0.10589713646632525
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00501 - p-value: 0.6866925642848738
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00445 - p-value: 0.1439340021417115
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00432 - p-value: 0.609512368192529
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00443 - p-value: 0.40107542039851796
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00442 - p-value: 0.4029994202368219
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00433 - p-value: 0.6635091848857968
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00429 - p-value: 0.4992413626160377
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00441 - p-value: 0.8492127140288965
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00450 - p-value: 0.4867828191068616
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00430 - p-value: 0.7050120405425861
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00440 - p-value: 0.9276462446556492
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00431 - p-value: 0.6069374830368126
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00439 - p-value: 0.92407679101592
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00438 - p-value: 0.4346057333880222
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00437 - p-value: 0.475204922038733
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00436 - p-value: 0.4407865111442988
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00435 - p-value: 0.6960096594143398
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00434 - p-value: 0.6009757726338087
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00504 - p-value: 0.45636251395769545
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00511 - p-value: 0.6046060934639899
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00451
Dropping newly added feature due to zero in interval: K00467
Dropping feature due to high p-value: K00427 - p-value: 0.6119784901689902
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00478 - p-value: 0.13700842160653964
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00477 - p-value: 0.7771263899601096
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00476 - p-value: 0.9398804145790844
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00475 - p-value: 0.7061180815518164
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00474 - p-value: 0.9697275341565842
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00473 - p-value: 0.5386100576640832
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00472 - p-value: 0.7291305779172426
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00471
Dropping newly added feature due to zero in interval: K00470
Dropping feature due to high p-value: K00469 - p-value: 0.9275846506157734
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00468
Dropping feature due to high p-value: K00516 - p-value: 0.5723867654632382
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00514 - p-value: 0.6299123690805613
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00466
Dropping newly added feature due to zero in interval: K00452
Dropping feature due to high p-value: K00465 - p-value: 0.553403007025667
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00464 - p-value: 0.7302597730826363
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00463 - p-value: 0.33381457647768864
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00462 - p-value: 0.7006439778641894
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00461 - p-value: 0.9275622790425962
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00460 - p-value: 0.6694137501612604
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00513 - p-value: 0.6850680829010101
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00512 - p-value: 0.6722195210399229
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00510 - p-value: 0.6259003871862536
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00509 - p-value: 0.6744780846830014
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00508 - p-value: 0.397702898641767
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00506 - p-value: 0.49213473720345025
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00505 - p-value: 0.8355367294715329
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00453 - p-value: 0.24439130624549654
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00428 - p-value: 0.5392685128410115
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00278 - p-value: 0.7322620175408185
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00426 - p-value: 0.6044294123329099
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00322
Dropping feature due to high p-value: K00002 - p-value: 0.22227562995278782
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00250 - p-value: 0.6662925629221581
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00251 - p-value: 0.7933874920136311
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00252 - p-value: 0.5213821916514447
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00253 - p-value: 0.367024016346987
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00254 - p-value: 0.415644122782564
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00255 - p-value: 0.5681124485224522
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00256 - p-value: 0.4035524787416388
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00269 - p-value: 0.4434883110215513
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00270 - p-value: 0.25281952957341913
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00271 - p-value: 0.20841102866079164
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00272 - p-value: 0.5492915081756828
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00273 - p-value: 0.1864752941270149
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00274 - p-value: 0.10258551819074314
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00275 - p-value: 0.9373751989695304
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00276 - p-value: 0.6153492891711279
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00277 - p-value: 0.8921242731077832
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00279 - p-value: 0.5208212693143783
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00280 - p-value: 0.9339869726803733
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00314 - p-value: 0.5546020663262858
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
7417.260007633073 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00315']
Dropping feature due to high p-value: K00315 - p-value: 0.34475790825808084
Dropping newly added feature due to high p-value: K00316 - p-value: 0.08668482064097137
Dropping feature due to high p-value: K00317 - p-value: 0.8583573255753792
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00318 - p-value: 0.3653372157977791
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00319 - p-value: 0.7805011197664813
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00248 - p-value: 0.5956044086761612
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00247 - p-value: 0.8357735631083916
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00246 - p-value: 0.19629625745852286
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00226 - p-value: 0.5780803949168418
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00005 - p-value: 0.18402056223452168
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00006 - p-value: 0.26161719346721135
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00052 - p-value: 0.13491176765490387
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00070 - p-value: 0.6572078017564182
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00071 - p-value: 0.49318179447619903
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00221 - p-value: 0.8441911788910093
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00222 - p-value: 0.32625536123467525
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00223 - p-value: 0.13476918024366816
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00224 - p-value: 0.7527002195815373
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00225 - p-value: 0.7333521740980189
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping newly added feature due to high p-value: K00227 - p-value: 0.05711662356529846
Dropping feature due to high p-value: K00245 - p-value: 0.4174145829364916
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00228 - p-value: 0.35017748589137165
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00229 - p-value: 0.6978540521795069
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00230 - p-value: 0.24506783609189586
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00231 - p-value: 0.5327668583609281
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00232 - p-value: 0.841555810539727
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00233 - p-value: 0.6172599622943844
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00234 - p-value: 0.7594664250323923
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00235 - p-value: 0.5443760510652531
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00236 - p-value: 0.7080907096225817
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00237 - p-value: 0.47976549287681114
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00238 - p-value: 0.4954981128514142
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00323 - p-value: 0.31688052618428497
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00425 - p-value: 0.6225907441806231
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00324 - p-value: 0.6581114095884815
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00390 - p-value: 0.2308049389602569
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping newly added feature due to zero in interval: K00391
Dropping feature due to high p-value: K00392 - p-value: 0.8005122134972532
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00393 - p-value: 0.4347095412431745
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00400 - p-value: 0.5206368636867875
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00401 - p-value: 0.38879279507555053
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00402 - p-value: 0.37627281460902706
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00403 - p-value: 0.43502505867078867
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00404 - p-value: 0.3465089410491542
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00405 - p-value: 0.3277758190102539
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00406 - p-value: 0.39340487571386207
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00409 - p-value: 0.1290256333040721
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00412 - p-value: 0.7772069651149762
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00413 - p-value: 0.6261343658351901
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00414 - p-value: 0.3299442622179729
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00415 - p-value: 0.9749874619881497
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00416 - p-value: 0.9431404922565013
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00417 - p-value: 0.4062613472012272
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00418 - p-value: 0.38691051911097285
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00419 - p-value: 0.2876295510302317
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00420 - p-value: 0.20338777918439743
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00421 - p-value: 0.8367882951231183
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00422 - p-value: 0.3967253568693896
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00423 - p-value: 0.21861904227438345
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00424 - p-value: 0.667678824625199
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00389 - p-value: 0.3938208329876738
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00388 - p-value: 0.9638279008843306
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00345 - p-value: 0.6298476690842909
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00325 - p-value: 0.23465167694170397
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00326 - p-value: 0.5311769226963218
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00327 - p-value: 0.8522351224162665
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00328 - p-value: 0.9322040246867869
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00329 - p-value: 0.23348963765953734
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00338 - p-value: 0.7148181533837987
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00339 - p-value: 0.6729996004678711
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00340 - p-value: 0.6347535906777577
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00341 - p-value: 0.7079969603628723
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00342 - p-value: 0.6778403780876028
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00343 - p-value: 0.6275541569403137
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00354 - p-value: 0.24268903949540932
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00385 - p-value: 0.47150527752142746
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00357 - p-value: 0.5631026252184069
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00370 - p-value: 0.7599429075517683
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00371 - p-value: 0.6545544138665655
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00372 - p-value: 0.5567987985198677
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00373 - p-value: 0.7626737074959108
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00374 - p-value: 0.6381538746932982
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00375 - p-value: 0.84215080027575
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00376 - p-value: 0.5074571945682292
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00377 - p-value: 0.7276153402166061
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00379 - p-value: 0.2827369196151214
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00380 - p-value: 0.38702906608355925
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                  label   No. Observations:                18983
Model:                          Logit   Df Residuals:                    18963
Method:                           MLE   Df Model:                           19
Date:                Sat, 16 Dec 2023   Pseudo R-squ.:                 0.05967
Time:                        18:33:42   Log-Likelihood:                -3683.0
converged:                       True   LL-Null:                       -3916.7
Covariance Type:            nonrobust   LLR p-value:                 3.739e-87
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const         -3.2022      0.047    -68.663      0.000      -3.294      -3.111
x1            -0.8605      0.153     -5.620      0.000      -1.161      -0.560
x2             0.5559      0.072      7.721      0.000       0.415       0.697
x3            -0.2353      0.062     -3.814      0.000      -0.356      -0.114
x4             0.2874      0.059      4.912      0.000       0.173       0.402
x5             0.1252      0.063      1.981      0.048       0.001       0.249
x6            -0.2208      0.102     -2.156      0.031      -0.422      -0.020
x7             0.1291      0.037      3.495      0.000       0.057       0.201
x8            -0.1284      0.062     -2.073      0.038      -0.250      -0.007
x9            -0.2124      0.077     -2.768      0.006      -0.363      -0.062
x10            0.1084      0.023      4.679      0.000       0.063       0.154
x11            0.1392      0.027      5.217      0.000       0.087       0.191
x12            0.0914      0.028      3.213      0.001       0.036       0.147
x13            0.3256      0.059      5.542      0.000       0.210       0.441
x14            0.0879      0.032      2.732      0.006       0.025       0.151
x15            0.1920      0.031      6.267      0.000       0.132       0.252
x16           -0.3174      0.070     -4.522      0.000      -0.455      -0.180
x17           -0.1813      0.074     -2.458      0.014      -0.326      -0.037
x18            0.0616      0.027      2.275      0.023       0.009       0.115
x19           -0.2189      0.095     -2.305      0.021      -0.405      -0.033
==============================================================================
Final selected features: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Tempo de execução: 360.5336821079254 segundos

# Run a logisticregression model with the selected features
start_timer()
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score

# Separate features (X) and target (y)
X = data[selected_features]
y = data["label"]

# RandomUnderSampler
#from imblearn.under_sampling import RandomUnderSampler
#sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# Resample
#X_train, y_train = sampler.fit_resample(X_train, y_train)

model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)

# Evaluate the model
yhat = model.predict(X_test)

# Performance Metrics
tpr = recall_score(y_test, yhat)
fpr = 1 - tpr
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)
ks = np.max(tpr - fpr)

print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)
print('KS: %.3f' % ks)
end_timer()

Accuracy: 55.07%
F1 Score: 0.139
Precision: 0.077
ROC AUC: 0.616
Balanced F1 Score: 0.667
Balanced Accuracy: 0.616
KS: 0.380
Tempo de execução: 7.078934907913208 segundos

Accuracy: 55.07% F1 Score: 0.139 Precision: 0.077 ROC AUC: 0.616 Balanced F1 Score: 0.667 Balanced Accuracy: 0.616 KS: 0.380 Tempo de execução: 5.4849231243133545 segundos

### Foward Feature Selection
import statsmodels.api as sm
import pandas as pd

# Copy the dataset and fill NaN values with 0
data = df_droped_001.copy()
data = data.fillna(0)

# Separate features (X) and target (y)
X = data.drop("label", axis=1)
y = data["label"]

# Create an empty list to store selected features
selected_features = []

# Iterate through features in the order of df_IV_001
for feature in df_IV_001['Feature']:
    # Add the current feature to the selected features list
    selected_features.append(feature)

    # Subset X with selected features
    X_selected = X[selected_features]

    scaler = StandardScaler()

    X_selected = scaler.fit_transform(X_selected)
    # Add constant column for intercept in logistic regression
    X_selected = sm.add_constant(X_selected)

    # Create and fit the logistic regression model
    model = sm.Logit(y, X_selected)
    result = model.fit_regularized(alpha=.5, trim_mode ='size' , method='l1', max_iter=100000000, disp=False)

    # Check if the optimization converged
    if result.mle_retvals['converged']:
        # Print summary of the logistic regression model
        print(result.aic,f"Features added: {selected_features}")
        print(result.summary())
    else:
        print(f"Features added: {selected_features}")
        print("Model did not converge\n")

# Print the final selected features
print("Final selected features:", selected_features)

# Install LightGBM Classifier
%pip install lightgbm

Collecting lightgbm
  Downloading lightgbm-4.2.0-py3-none-win_amd64.whl.metadata (19 kB)
Requirement already satisfied: numpy in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from lightgbm) (1.26.2)
Requirement already satisfied: scipy in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from lightgbm) (1.11.4)
Downloading lightgbm-4.2.0-py3-none-win_amd64.whl (1.3 MB)
   ---------------------------------------- 0.0/1.3 MB ? eta -:--:--
   ---------------------------------------- 0.0/1.3 MB ? eta -:--:--
   - -------------------------------------- 0.0/1.3 MB 495.5 kB/s eta 0:00:03
   --- ------------------------------------ 0.1/1.3 MB 939.4 kB/s eta 0:00:02
   ----- ---------------------------------- 0.2/1.3 MB 1.1 MB/s eta 0:00:02
   ------ --------------------------------- 0.2/1.3 MB 1.1 MB/s eta 0:00:01
   --------- ------------------------------ 0.3/1.3 MB 1.1 MB/s eta 0:00:01
   ---------- ----------------------------- 0.4/1.3 MB 1.2 MB/s eta 0:00:01
   ------------- -------------------------- 0.4/1.3 MB 1.3 MB/s eta 0:00:01
   --------------- ------------------------ 0.5/1.3 MB 1.3 MB/s eta 0:00:01
   ---------------- ----------------------- 0.6/1.3 MB 1.2 MB/s eta 0:00:01
   ----------------- ---------------------- 0.6/1.3 MB 1.2 MB/s eta 0:00:01
   ------------------- -------------------- 0.7/1.3 MB 1.2 MB/s eta 0:00:01
   ---------------------- ----------------- 0.7/1.3 MB 1.3 MB/s eta 0:00:01
   ------------------------- -------------- 0.8/1.3 MB 1.3 MB/s eta 0:00:01
   --------------------------- ------------ 0.9/1.3 MB 1.4 MB/s eta 0:00:01
   ----------------------------- ---------- 1.0/1.3 MB 1.4 MB/s eta 0:00:01
   -------------------------------- ------- 1.1/1.3 MB 1.4 MB/s eta 0:00:01
   --------------------------------- ------ 1.1/1.3 MB 1.4 MB/s eta 0:00:01
   ------------------------------------ --- 1.2/1.3 MB 1.4 MB/s eta 0:00:01
   ---------------------------------------  1.3/1.3 MB 1.4 MB/s eta 0:00:01
   ---------------------------------------- 1.3/1.3 MB 1.4 MB/s eta 0:00:00
Installing collected packages: lightgbm
Successfully installed lightgbm-4.2.0
Note: you may need to restart the kernel to use updated packages.

9. Seleção de Modelo¶

Com o propósito em aprender (na prática), extrair o máximo de informações dos dados e aplicar o conhecimento adquirido durante o curso, optamos por avaliar diversos algoritmos para selecionar o mais adequado ao problema em questão. Essa abordagem nos possibilita escolher o modelo que melhor se adapta às características específicas do nosso conjunto de dados, visando obter resultados mais otimizados nas métricas. Abaixo estão os algoritmos que foram avaliados:

Logistic Regression
k-Nearest Neighbors (k-NN)
Support Vector Machines (SVM)
Neural Networks
Naive Bayes
Decision Trees
Random Forest
Gradient Boosting Machines (GBM)
XGBoost
LightGBM
CatBoost
HistGradientBoostingClassifier

df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False)
df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1)
df_cleandata

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[3], line 1
----> 1 df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False)
      2 df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1)
      3 df_cleandata

NameError: name 'pd' is not defined

df_droped_001 = pd.read_csv(f'{files_directory}/df_droped_001.csv', index_col=False)
df_droped_001

df = df_droped_001.copy()
df.columns[479]

'count_of_zero'

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
import os

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the hyperparameters for each model
hyperparameters = {
    'Logistic Regression': {'C': [0.1, 1, 10]},
    'k-Nearest Neighbors (k-NN)': {'n_neighbors': [3, 5, 7]},
    'Support Vector Machines (SVM)': {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']},
    'Neural Networks': {'hidden_layer_sizes': [(50,), (100,), (50, 50)]},
    'Naive Bayes': {},
    'Decision Trees': {'max_depth': [None, 10, 20]},
    'Random Forest': {'n_estimators': [50, 100, 200]},
    'Gradient Boosting Machines (GBM)': {'n_estimators': [50, 100, 200]},
    'XGBoost': {'n_estimators': [50, 100, 200]},
    'LightGBM': {'n_estimators': [50, 100, 200]}
}

# Define the imbalanced data handling techniques
sampling_techniques = {
    'Oversampling': RandomOverSampler(),
    'Undersampling': RandomUnderSampler(),
    'Hybrid Sampling (SMOTEENN)': SMOTEENN(),
    'Hybrid Sampling (SMOTETomek)': SMOTETomek()
}

# Define the imputation techniques
imputation_techniques = {
    'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
    'Mean Imputation': SimpleImputer(strategy='mean'),
    'Median Imputation': SimpleImputer(strategy='median')
}

# Define a function to calculate KS and Gini
def calculate_ks_gini(y_true, y_prob):
    fpr, tpr, thresholds = roc_curve(y_true, y_prob)
    ks = max(tpr - fpr)
    gini = 2 * auc(fpr, tpr) - 1
    return ks, gini

# Create a list to store the results
results = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [
    ('Logistic Regression', LogisticRegression()),
    ('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
    ('Support Vector Machines (SVM)', SVC(probability=True)),
    ('Neural Networks', MLPClassifier()),
    ('Naive Bayes', GaussianNB()),
    ('Decision Trees', DecisionTreeClassifier()),
    ('Random Forest', RandomForestClassifier()),
    ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
    ('XGBoost', XGBClassifier()),
    ('LightGBM', LGBMClassifier())
]:
    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():
        # Iterate through each imputation technique
        for imputation_name, imputation_technique in imputation_techniques.items():
            # Create a new dataframe to store the results for this model, technique combination
            df_result = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])

            # Perform k-fold cross-validation
            for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                # Apply imputation technique on the train and test sets
                X_train_imputed = imputation_technique.fit_transform(X_train)
                X_test_imputed = imputation_technique.transform(X_test)

                # Apply the imbalanced data handling technique on the train set
                X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)

                # Train the model on the resampled train set
                model.fit(X_train_resampled, y_train_resampled)

                # Make predictions on the test set
                y_pred = model.predict(X_test_imputed)
                y_prob = model.predict_proba(X_test_imputed)[:, 1]

                # Calculate the evaluation metrics
                metrics = {
                    'Accuracy': accuracy_score(y_test, y_pred),
                    'Precision': precision_score(y_test, y_pred),
                    'Recall': recall_score(y_test, y_pred),
                    'F1-score': f1_score(y_test, y_pred),
                    'AUC': roc_auc_score(y_test, y_prob)
                }

                # Calculate KS and Gini
                ks, gini = calculate_ks_gini(y_test, y_prob)
                metrics['KS'] = ks
                metrics['Gini'] = gini

                # Append the results to the dataframe
                df_result = pd.concat([df_result, pd.DataFrame({
                    'Algorithm': model_name,
                    'Metrics': metrics,
                    'Hyperparameters': model.get_params(),
                    'Imputation Technique': imputation_name,
                    'Imbalance Class Technique': sampling_name,
                    'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}"
                 })], ignore_index=True, axis=0)

            # Append the results for this model, technique combination to the overall results list
            results.append(df_result)

# Combine all the results dataframes into one final dataframe
df_results_final = pd.concat(results, ignore_index=True)

# Save each model in a separate file with name corresponding to model unique code
for model_unique_code in df_results_final['Model Unique Code'].unique():
    df_model = df_results_final[df_results_final['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}.csv", index=False)

# Display the final dataframe
print(df_results_final)

[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61079
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61069
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61075
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031740 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61348
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61052
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60775
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60808
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60542
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61184
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60577
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60857
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60919
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60903
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61157
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60896
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 25537
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 360
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27631
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26986
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 366
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26277
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 25705
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32150
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34012
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33675
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007749 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32578
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32425
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29267
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 366
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30965
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30175
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30115
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29968
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 12129, number of negative: 9855
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94789
[LightGBM] [Info] Number of data points in the train set: 21984, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.551719 -> initscore=0.207620
[LightGBM] [Info] Start training from score 0.207620
[LightGBM] [Info] Number of positive: 12069, number of negative: 9597
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94279
[LightGBM] [Info] Number of data points in the train set: 21666, number of used features: 450
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.557048 -> initscore=0.229190
[LightGBM] [Info] Start training from score 0.229190
[LightGBM] [Info] Number of positive: 12393, number of negative: 9808
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95223
[LightGBM] [Info] Number of data points in the train set: 22201, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.558218 -> initscore=0.233933
[LightGBM] [Info] Start training from score 0.233933
[LightGBM] [Info] Number of positive: 12157, number of negative: 9790
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94458
[LightGBM] [Info] Number of data points in the train set: 21947, number of used features: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.553925 -> initscore=0.216544
[LightGBM] [Info] Start training from score 0.216544
[LightGBM] [Info] Number of positive: 12207, number of negative: 9770
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94284
[LightGBM] [Info] Number of data points in the train set: 21977, number of used features: 453
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.555444 -> initscore=0.222693
[LightGBM] [Info] Start training from score 0.222693
[LightGBM] [Info] Number of positive: 12159, number of negative: 10161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120694
[LightGBM] [Info] Number of data points in the train set: 22320, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544758 -> initscore=0.179513
[LightGBM] [Info] Start training from score 0.179513
[LightGBM] [Info] Number of positive: 12119, number of negative: 9928
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 121190
[LightGBM] [Info] Number of data points in the train set: 22047, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.549689 -> initscore=0.199415
[LightGBM] [Info] Start training from score 0.199415
[LightGBM] [Info] Number of positive: 12292, number of negative: 10028
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120588
[LightGBM] [Info] Number of data points in the train set: 22320, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550717 -> initscore=0.203567
[LightGBM] [Info] Start training from score 0.203567
[LightGBM] [Info] Number of positive: 12077, number of negative: 10178
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.061489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120890
[LightGBM] [Info] Number of data points in the train set: 22255, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.542665 -> initscore=0.171074
[LightGBM] [Info] Start training from score 0.171074
[LightGBM] [Info] Number of positive: 12285, number of negative: 10113
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120685
[LightGBM] [Info] Number of data points in the train set: 22398, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.548486 -> initscore=0.194557
[LightGBM] [Info] Start training from score 0.194557
[LightGBM] [Info] Number of positive: 12057, number of negative: 9953
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95280
[LightGBM] [Info] Number of data points in the train set: 22010, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.547796 -> initscore=0.191771
[LightGBM] [Info] Start training from score 0.191771
[LightGBM] [Info] Number of positive: 12091, number of negative: 9699
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 93706
[LightGBM] [Info] Number of data points in the train set: 21790, number of used features: 448
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.554888 -> initscore=0.220439
[LightGBM] [Info] Start training from score 0.220439
[LightGBM] [Info] Number of positive: 12315, number of negative: 9885
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95109
[LightGBM] [Info] Number of data points in the train set: 22200, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.554730 -> initscore=0.219800
[LightGBM] [Info] Start training from score 0.219800
[LightGBM] [Info] Number of positive: 12055, number of negative: 9804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039955 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94328
[LightGBM] [Info] Number of data points in the train set: 21859, number of used features: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.551489 -> initscore=0.206689
[LightGBM] [Info] Start training from score 0.206689
[LightGBM] [Info] Number of positive: 12308, number of negative: 9926
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94001
[LightGBM] [Info] Number of data points in the train set: 22234, number of used features: 453
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.553567 -> initscore=0.215092
[LightGBM] [Info] Start training from score 0.215092
[LightGBM] [Info] Number of positive: 14204, number of negative: 14204
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96056
[LightGBM] [Info] Number of data points in the train set: 28408, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14161, number of negative: 14161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95064
[LightGBM] [Info] Number of data points in the train set: 28322, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14166, number of negative: 14166
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96704
[LightGBM] [Info] Number of data points in the train set: 28332, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14204, number of negative: 14204
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95996
[LightGBM] [Info] Number of data points in the train set: 28408, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14218, number of negative: 14218
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95900
[LightGBM] [Info] Number of data points in the train set: 28436, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14249, number of negative: 14249
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.065897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120798
[LightGBM] [Info] Number of data points in the train set: 28498, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14198, number of negative: 14198
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120954
[LightGBM] [Info] Number of data points in the train set: 28396, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14212, number of negative: 14212
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120937
[LightGBM] [Info] Number of data points in the train set: 28424, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14210, number of negative: 14210
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120988
[LightGBM] [Info] Number of data points in the train set: 28420, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14237, number of negative: 14237
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120426
[LightGBM] [Info] Number of data points in the train set: 28474, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14224, number of negative: 14224
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96402
[LightGBM] [Info] Number of data points in the train set: 28448, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14178, number of negative: 14178
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94950
[LightGBM] [Info] Number of data points in the train set: 28356, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14192, number of negative: 14192
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96383
[LightGBM] [Info] Number of data points in the train set: 28384, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14174, number of negative: 14174
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96036
[LightGBM] [Info] Number of data points in the train set: 28348, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14217, number of negative: 14217
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95550
[LightGBM] [Info] Number of data points in the train set: 28434, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
                 Algorithm   Metrics Hyperparameters Imputation Technique  \
0      Logistic Regression  0.494864             NaN      Zero Imputation   
1      Logistic Regression  0.086572             NaN      Zero Imputation   
2      Logistic Regression  0.742616             NaN      Zero Imputation   
3      Logistic Regression  0.155066             NaN      Zero Imputation   
4      Logistic Regression  0.623146             NaN      Zero Imputation   
...                    ...       ...             ...                  ...   
14455             LightGBM       NaN             0.0    Median Imputation   
14456             LightGBM       NaN             0.0    Median Imputation   
14457             LightGBM       NaN             1.0    Median Imputation   
14458             LightGBM       NaN          200000    Median Imputation   
14459             LightGBM       NaN               0    Median Imputation   

          Imbalance Class Technique  \
0                      Oversampling   
1                      Oversampling   
2                      Oversampling   
3                      Oversampling   
4                      Oversampling   
...                             ...   
14455  Hybrid Sampling (SMOTETomek)   
14456  Hybrid Sampling (SMOTETomek)   
14457  Hybrid Sampling (SMOTETomek)   
14458  Hybrid Sampling (SMOTETomek)   
14459  Hybrid Sampling (SMOTETomek)   

                                       Model Unique Code  
0      Logistic Regression_Oversampling_Zero Imputati...  
1      Logistic Regression_Oversampling_Zero Imputati...  
2      Logistic Regression_Oversampling_Zero Imputati...  
3      Logistic Regression_Oversampling_Zero Imputati...  
4      Logistic Regression_Oversampling_Zero Imputati...  
...                                                  ...  
14455  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
14456  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
14457  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
14458  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
14459  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  

[14460 rows x 6 columns]

display(results)

[               Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.494864             NaN      Zero Imputation   
 1    Logistic Regression  0.086572             NaN      Zero Imputation   
 2    Logistic Regression  0.742616             NaN      Zero Imputation   
 3    Logistic Regression  0.155066             NaN      Zero Imputation   
 4    Logistic Regression  0.623146             NaN      Zero Imputation   
 5    Logistic Regression  0.230842             NaN      Zero Imputation   
 6    Logistic Regression  0.246291             NaN      Zero Imputation   
 7    Logistic Regression       NaN             1.0      Zero Imputation   
 8    Logistic Regression       NaN            None      Zero Imputation   
 9    Logistic Regression       NaN           False      Zero Imputation   
 10   Logistic Regression       NaN            True      Zero Imputation   
 11   Logistic Regression       NaN               1      Zero Imputation   
 12   Logistic Regression       NaN            None      Zero Imputation   
 13   Logistic Regression       NaN             100      Zero Imputation   
 14   Logistic Regression       NaN            auto      Zero Imputation   
 15   Logistic Regression       NaN            None      Zero Imputation   
 16   Logistic Regression       NaN              l2      Zero Imputation   
 17   Logistic Regression       NaN            None      Zero Imputation   
 18   Logistic Regression       NaN           lbfgs      Zero Imputation   
 19   Logistic Regression       NaN          0.0001      Zero Imputation   
 20   Logistic Regression       NaN               0      Zero Imputation   
 21   Logistic Regression       NaN           False      Zero Imputation   
 22   Logistic Regression  0.460890             NaN      Zero Imputation   
 23   Logistic Regression  0.058603             NaN      Zero Imputation   
 24   Logistic Regression  0.762195             NaN      Zero Imputation   
 25   Logistic Regression  0.108838             NaN      Zero Imputation   
 26   Logistic Regression  0.648632             NaN      Zero Imputation   
 27   Logistic Regression  0.236828             NaN      Zero Imputation   
 28   Logistic Regression  0.297263             NaN      Zero Imputation   
 29   Logistic Regression       NaN             1.0      Zero Imputation   
 30   Logistic Regression       NaN            None      Zero Imputation   
 31   Logistic Regression       NaN           False      Zero Imputation   
 32   Logistic Regression       NaN            True      Zero Imputation   
 33   Logistic Regression       NaN               1      Zero Imputation   
 34   Logistic Regression       NaN            None      Zero Imputation   
 35   Logistic Regression       NaN             100      Zero Imputation   
 36   Logistic Regression       NaN            auto      Zero Imputation   
 37   Logistic Regression       NaN            None      Zero Imputation   
 38   Logistic Regression       NaN              l2      Zero Imputation   
 39   Logistic Regression       NaN            None      Zero Imputation   
 40   Logistic Regression       NaN           lbfgs      Zero Imputation   
 41   Logistic Regression       NaN          0.0001      Zero Imputation   
 42   Logistic Regression       NaN               0      Zero Imputation   
 43   Logistic Regression       NaN           False      Zero Imputation   
 44   Logistic Regression  0.454043             NaN      Zero Imputation   
 45   Logistic Regression  0.063426             NaN      Zero Imputation   
 46   Logistic Regression  0.732620             NaN      Zero Imputation   
 47   Logistic Regression  0.116745             NaN      Zero Imputation   
 48   Logistic Regression  0.641625             NaN      Zero Imputation   
 49   Logistic Regression  0.207897             NaN      Zero Imputation   
 50   Logistic Regression  0.283251             NaN      Zero Imputation   
 51   Logistic Regression       NaN             1.0      Zero Imputation   
 52   Logistic Regression       NaN            None      Zero Imputation   
 53   Logistic Regression       NaN           False      Zero Imputation   
 54   Logistic Regression       NaN            True      Zero Imputation   
 55   Logistic Regression       NaN               1      Zero Imputation   
 56   Logistic Regression       NaN            None      Zero Imputation   
 57   Logistic Regression       NaN             100      Zero Imputation   
 58   Logistic Regression       NaN            auto      Zero Imputation   
 59   Logistic Regression       NaN            None      Zero Imputation   
 60   Logistic Regression       NaN              l2      Zero Imputation   
 61   Logistic Regression       NaN            None      Zero Imputation   
 62   Logistic Regression       NaN           lbfgs      Zero Imputation   
 63   Logistic Regression       NaN          0.0001      Zero Imputation   
 64   Logistic Regression       NaN               0      Zero Imputation   
 65   Logistic Regression       NaN           False      Zero Imputation   
 66   Logistic Regression  0.448894             NaN      Zero Imputation   
 67   Logistic Regression  0.068699             NaN      Zero Imputation   
 68   Logistic Regression  0.770408             NaN      Zero Imputation   
 69   Logistic Regression  0.126149             NaN      Zero Imputation   
 70   Logistic Regression  0.661925             NaN      Zero Imputation   
 71   Logistic Regression  0.246003             NaN      Zero Imputation   
 72   Logistic Regression  0.323851             NaN      Zero Imputation   
 73   Logistic Regression       NaN             1.0      Zero Imputation   
 74   Logistic Regression       NaN            None      Zero Imputation   
 75   Logistic Regression       NaN           False      Zero Imputation   
 76   Logistic Regression       NaN            True      Zero Imputation   
 77   Logistic Regression       NaN               1      Zero Imputation   
 78   Logistic Regression       NaN            None      Zero Imputation   
 79   Logistic Regression       NaN             100      Zero Imputation   
 80   Logistic Regression       NaN            auto      Zero Imputation   
 81   Logistic Regression       NaN            None      Zero Imputation   
 82   Logistic Regression       NaN              l2      Zero Imputation   
 83   Logistic Regression       NaN            None      Zero Imputation   
 84   Logistic Regression       NaN           lbfgs      Zero Imputation   
 85   Logistic Regression       NaN          0.0001      Zero Imputation   
 86   Logistic Regression       NaN               0      Zero Imputation   
 87   Logistic Regression       NaN           False      Zero Imputation   
 88   Logistic Regression  0.468651             NaN      Zero Imputation   
 89   Logistic Regression  0.075436             NaN      Zero Imputation   
 90   Logistic Regression  0.740741             NaN      Zero Imputation   
 91   Logistic Regression  0.136928             NaN      Zero Imputation   
 92   Logistic Regression  0.658384             NaN      Zero Imputation   
 93   Logistic Regression  0.251971             NaN      Zero Imputation   
 94   Logistic Regression  0.316768             NaN      Zero Imputation   
 95   Logistic Regression       NaN             1.0      Zero Imputation   
 96   Logistic Regression       NaN            None      Zero Imputation   
 97   Logistic Regression       NaN           False      Zero Imputation   
 98   Logistic Regression       NaN            True      Zero Imputation   
 99   Logistic Regression       NaN               1      Zero Imputation   
 100  Logistic Regression       NaN            None      Zero Imputation   
 101  Logistic Regression       NaN             100      Zero Imputation   
 102  Logistic Regression       NaN            auto      Zero Imputation   
 103  Logistic Regression       NaN            None      Zero Imputation   
 104  Logistic Regression       NaN              l2      Zero Imputation   
 105  Logistic Regression       NaN            None      Zero Imputation   
 106  Logistic Regression       NaN           lbfgs      Zero Imputation   
 107  Logistic Regression       NaN          0.0001      Zero Imputation   
 108  Logistic Regression       NaN               0      Zero Imputation   
 109  Logistic Regression       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Oversampling_Zero Imputati...  
 1    Logistic Regression_Oversampling_Zero Imputati...  
 2    Logistic Regression_Oversampling_Zero Imputati...  
 3    Logistic Regression_Oversampling_Zero Imputati...  
 4    Logistic Regression_Oversampling_Zero Imputati...  
 5    Logistic Regression_Oversampling_Zero Imputati...  
 6    Logistic Regression_Oversampling_Zero Imputati...  
 7    Logistic Regression_Oversampling_Zero Imputati...  
 8    Logistic Regression_Oversampling_Zero Imputati...  
 9    Logistic Regression_Oversampling_Zero Imputati...  
 10   Logistic Regression_Oversampling_Zero Imputati...  
 11   Logistic Regression_Oversampling_Zero Imputati...  
 12   Logistic Regression_Oversampling_Zero Imputati...  
 13   Logistic Regression_Oversampling_Zero Imputati...  
 14   Logistic Regression_Oversampling_Zero Imputati...  
 15   Logistic Regression_Oversampling_Zero Imputati...  
 16   Logistic Regression_Oversampling_Zero Imputati...  
 17   Logistic Regression_Oversampling_Zero Imputati...  
 18   Logistic Regression_Oversampling_Zero Imputati...  
 19   Logistic Regression_Oversampling_Zero Imputati...  
 20   Logistic Regression_Oversampling_Zero Imputati...  
 21   Logistic Regression_Oversampling_Zero Imputati...  
 22   Logistic Regression_Oversampling_Zero Imputati...  
 23   Logistic Regression_Oversampling_Zero Imputati...  
 24   Logistic Regression_Oversampling_Zero Imputati...  
 25   Logistic Regression_Oversampling_Zero Imputati...  
 26   Logistic Regression_Oversampling_Zero Imputati...  
 27   Logistic Regression_Oversampling_Zero Imputati...  
 28   Logistic Regression_Oversampling_Zero Imputati...  
 29   Logistic Regression_Oversampling_Zero Imputati...  
 30   Logistic Regression_Oversampling_Zero Imputati...  
 31   Logistic Regression_Oversampling_Zero Imputati...  
 32   Logistic Regression_Oversampling_Zero Imputati...  
 33   Logistic Regression_Oversampling_Zero Imputati...  
 34   Logistic Regression_Oversampling_Zero Imputati...  
 35   Logistic Regression_Oversampling_Zero Imputati...  
 36   Logistic Regression_Oversampling_Zero Imputati...  
 37   Logistic Regression_Oversampling_Zero Imputati...  
 38   Logistic Regression_Oversampling_Zero Imputati...  
 39   Logistic Regression_Oversampling_Zero Imputati...  
 40   Logistic Regression_Oversampling_Zero Imputati...  
 41   Logistic Regression_Oversampling_Zero Imputati...  
 42   Logistic Regression_Oversampling_Zero Imputati...  
 43   Logistic Regression_Oversampling_Zero Imputati...  
 44   Logistic Regression_Oversampling_Zero Imputati...  
 45   Logistic Regression_Oversampling_Zero Imputati...  
 46   Logistic Regression_Oversampling_Zero Imputati...  
 47   Logistic Regression_Oversampling_Zero Imputati...  
 48   Logistic Regression_Oversampling_Zero Imputati...  
 49   Logistic Regression_Oversampling_Zero Imputati...  
 50   Logistic Regression_Oversampling_Zero Imputati...  
 51   Logistic Regression_Oversampling_Zero Imputati...  
 52   Logistic Regression_Oversampling_Zero Imputati...  
 53   Logistic Regression_Oversampling_Zero Imputati...  
 54   Logistic Regression_Oversampling_Zero Imputati...  
 55   Logistic Regression_Oversampling_Zero Imputati...  
 56   Logistic Regression_Oversampling_Zero Imputati...  
 57   Logistic Regression_Oversampling_Zero Imputati...  
 58   Logistic Regression_Oversampling_Zero Imputati...  
 59   Logistic Regression_Oversampling_Zero Imputati...  
 60   Logistic Regression_Oversampling_Zero Imputati...  
 61   Logistic Regression_Oversampling_Zero Imputati...  
 62   Logistic Regression_Oversampling_Zero Imputati...  
 63   Logistic Regression_Oversampling_Zero Imputati...  
 64   Logistic Regression_Oversampling_Zero Imputati...  
 65   Logistic Regression_Oversampling_Zero Imputati...  
 66   Logistic Regression_Oversampling_Zero Imputati...  
 67   Logistic Regression_Oversampling_Zero Imputati...  
 68   Logistic Regression_Oversampling_Zero Imputati...  
 69   Logistic Regression_Oversampling_Zero Imputati...  
 70   Logistic Regression_Oversampling_Zero Imputati...  
 71   Logistic Regression_Oversampling_Zero Imputati...  
 72   Logistic Regression_Oversampling_Zero Imputati...  
 73   Logistic Regression_Oversampling_Zero Imputati...  
 74   Logistic Regression_Oversampling_Zero Imputati...  
 75   Logistic Regression_Oversampling_Zero Imputati...  
 76   Logistic Regression_Oversampling_Zero Imputati...  
 77   Logistic Regression_Oversampling_Zero Imputati...  
 78   Logistic Regression_Oversampling_Zero Imputati...  
 79   Logistic Regression_Oversampling_Zero Imputati...  
 80   Logistic Regression_Oversampling_Zero Imputati...  
 81   Logistic Regression_Oversampling_Zero Imputati...  
 82   Logistic Regression_Oversampling_Zero Imputati...  
 83   Logistic Regression_Oversampling_Zero Imputati...  
 84   Logistic Regression_Oversampling_Zero Imputati...  
 85   Logistic Regression_Oversampling_Zero Imputati...  
 86   Logistic Regression_Oversampling_Zero Imputati...  
 87   Logistic Regression_Oversampling_Zero Imputati...  
 88   Logistic Regression_Oversampling_Zero Imputati...  
 89   Logistic Regression_Oversampling_Zero Imputati...  
 90   Logistic Regression_Oversampling_Zero Imputati...  
 91   Logistic Regression_Oversampling_Zero Imputati...  
 92   Logistic Regression_Oversampling_Zero Imputati...  
 93   Logistic Regression_Oversampling_Zero Imputati...  
 94   Logistic Regression_Oversampling_Zero Imputati...  
 95   Logistic Regression_Oversampling_Zero Imputati...  
 96   Logistic Regression_Oversampling_Zero Imputati...  
 97   Logistic Regression_Oversampling_Zero Imputati...  
 98   Logistic Regression_Oversampling_Zero Imputati...  
 99   Logistic Regression_Oversampling_Zero Imputati...  
 100  Logistic Regression_Oversampling_Zero Imputati...  
 101  Logistic Regression_Oversampling_Zero Imputati...  
 102  Logistic Regression_Oversampling_Zero Imputati...  
 103  Logistic Regression_Oversampling_Zero Imputati...  
 104  Logistic Regression_Oversampling_Zero Imputati...  
 105  Logistic Regression_Oversampling_Zero Imputati...  
 106  Logistic Regression_Oversampling_Zero Imputati...  
 107  Logistic Regression_Oversampling_Zero Imputati...  
 108  Logistic Regression_Oversampling_Zero Imputati...  
 109  Logistic Regression_Oversampling_Zero Imputati...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.436134             NaN      Mean Imputation   
 1    Logistic Regression  0.080617             NaN      Mean Imputation   
 2    Logistic Regression  0.772152             NaN      Mean Imputation   
 3    Logistic Regression  0.145991             NaN      Mean Imputation   
 4    Logistic Regression  0.635176             NaN      Mean Imputation   
 5    Logistic Regression  0.239617             NaN      Mean Imputation   
 6    Logistic Regression  0.270352             NaN      Mean Imputation   
 7    Logistic Regression       NaN             1.0      Mean Imputation   
 8    Logistic Regression       NaN            None      Mean Imputation   
 9    Logistic Regression       NaN           False      Mean Imputation   
 10   Logistic Regression       NaN            True      Mean Imputation   
 11   Logistic Regression       NaN               1      Mean Imputation   
 12   Logistic Regression       NaN            None      Mean Imputation   
 13   Logistic Regression       NaN             100      Mean Imputation   
 14   Logistic Regression       NaN            auto      Mean Imputation   
 15   Logistic Regression       NaN            None      Mean Imputation   
 16   Logistic Regression       NaN              l2      Mean Imputation   
 17   Logistic Regression       NaN            None      Mean Imputation   
 18   Logistic Regression       NaN           lbfgs      Mean Imputation   
 19   Logistic Regression       NaN          0.0001      Mean Imputation   
 20   Logistic Regression       NaN               0      Mean Imputation   
 21   Logistic Regression       NaN           False      Mean Imputation   
 22   Logistic Regression  0.459310             NaN      Mean Imputation   
 23   Logistic Regression  0.057196             NaN      Mean Imputation   
 24   Logistic Regression  0.743902             NaN      Mean Imputation   
 25   Logistic Regression  0.106226             NaN      Mean Imputation   
 26   Logistic Regression  0.623355             NaN      Mean Imputation   
 27   Logistic Regression  0.216734             NaN      Mean Imputation   
 28   Logistic Regression  0.246710             NaN      Mean Imputation   
 29   Logistic Regression       NaN             1.0      Mean Imputation   
 30   Logistic Regression       NaN            None      Mean Imputation   
 31   Logistic Regression       NaN           False      Mean Imputation   
 32   Logistic Regression       NaN            True      Mean Imputation   
 33   Logistic Regression       NaN               1      Mean Imputation   
 34   Logistic Regression       NaN            None      Mean Imputation   
 35   Logistic Regression       NaN             100      Mean Imputation   
 36   Logistic Regression       NaN            auto      Mean Imputation   
 37   Logistic Regression       NaN            None      Mean Imputation   
 38   Logistic Regression       NaN              l2      Mean Imputation   
 39   Logistic Regression       NaN            None      Mean Imputation   
 40   Logistic Regression       NaN           lbfgs      Mean Imputation   
 41   Logistic Regression       NaN          0.0001      Mean Imputation   
 42   Logistic Regression       NaN               0      Mean Imputation   
 43   Logistic Regression       NaN           False      Mean Imputation   
 44   Logistic Regression  0.451936             NaN      Mean Imputation   
 45   Logistic Regression  0.064798             NaN      Mean Imputation   
 46   Logistic Regression  0.754011             NaN      Mean Imputation   
 47   Logistic Regression  0.119340             NaN      Mean Imputation   
 48   Logistic Regression  0.637962             NaN      Mean Imputation   
 49   Logistic Regression  0.225584             NaN      Mean Imputation   
 50   Logistic Regression  0.275924             NaN      Mean Imputation   
 51   Logistic Regression       NaN             1.0      Mean Imputation   
 52   Logistic Regression       NaN            None      Mean Imputation   
 53   Logistic Regression       NaN           False      Mean Imputation   
 54   Logistic Regression       NaN            True      Mean Imputation   
 55   Logistic Regression       NaN               1      Mean Imputation   
 56   Logistic Regression       NaN            None      Mean Imputation   
 57   Logistic Regression       NaN             100      Mean Imputation   
 58   Logistic Regression       NaN            auto      Mean Imputation   
 59   Logistic Regression       NaN            None      Mean Imputation   
 60   Logistic Regression       NaN              l2      Mean Imputation   
 61   Logistic Regression       NaN            None      Mean Imputation   
 62   Logistic Regression       NaN           lbfgs      Mean Imputation   
 63   Logistic Regression       NaN          0.0001      Mean Imputation   
 64   Logistic Regression       NaN               0      Mean Imputation   
 65   Logistic Regression       NaN           False      Mean Imputation   
 66   Logistic Regression  0.482350             NaN      Mean Imputation   
 67   Logistic Regression  0.071255             NaN      Mean Imputation   
 68   Logistic Regression  0.750000             NaN      Mean Imputation   
 69   Logistic Regression  0.130146             NaN      Mean Imputation   
 70   Logistic Regression  0.644099             NaN      Mean Imputation   
 71   Logistic Regression  0.252166             NaN      Mean Imputation   
 72   Logistic Regression  0.288199             NaN      Mean Imputation   
 73   Logistic Regression       NaN             1.0      Mean Imputation   
 74   Logistic Regression       NaN            None      Mean Imputation   
 75   Logistic Regression       NaN           False      Mean Imputation   
 76   Logistic Regression       NaN            True      Mean Imputation   
 77   Logistic Regression       NaN               1      Mean Imputation   
 78   Logistic Regression       NaN            None      Mean Imputation   
 79   Logistic Regression       NaN             100      Mean Imputation   
 80   Logistic Regression       NaN            auto      Mean Imputation   
 81   Logistic Regression       NaN            None      Mean Imputation   
 82   Logistic Regression       NaN              l2      Mean Imputation   
 83   Logistic Regression       NaN            None      Mean Imputation   
 84   Logistic Regression       NaN           lbfgs      Mean Imputation   
 85   Logistic Regression       NaN          0.0001      Mean Imputation   
 86   Logistic Regression       NaN               0      Mean Imputation   
 87   Logistic Regression       NaN           False      Mean Imputation   
 88   Logistic Regression  0.465490             NaN      Mean Imputation   
 89   Logistic Regression  0.075012             NaN      Mean Imputation   
 90   Logistic Regression  0.740741             NaN      Mean Imputation   
 91   Logistic Regression  0.136228             NaN      Mean Imputation   
 92   Logistic Regression  0.648727             NaN      Mean Imputation   
 93   Logistic Regression  0.246343             NaN      Mean Imputation   
 94   Logistic Regression  0.297454             NaN      Mean Imputation   
 95   Logistic Regression       NaN             1.0      Mean Imputation   
 96   Logistic Regression       NaN            None      Mean Imputation   
 97   Logistic Regression       NaN           False      Mean Imputation   
 98   Logistic Regression       NaN            True      Mean Imputation   
 99   Logistic Regression       NaN               1      Mean Imputation   
 100  Logistic Regression       NaN            None      Mean Imputation   
 101  Logistic Regression       NaN             100      Mean Imputation   
 102  Logistic Regression       NaN            auto      Mean Imputation   
 103  Logistic Regression       NaN            None      Mean Imputation   
 104  Logistic Regression       NaN              l2      Mean Imputation   
 105  Logistic Regression       NaN            None      Mean Imputation   
 106  Logistic Regression       NaN           lbfgs      Mean Imputation   
 107  Logistic Regression       NaN          0.0001      Mean Imputation   
 108  Logistic Regression       NaN               0      Mean Imputation   
 109  Logistic Regression       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Oversampling_Mean Imputati...  
 1    Logistic Regression_Oversampling_Mean Imputati...  
 2    Logistic Regression_Oversampling_Mean Imputati...  
 3    Logistic Regression_Oversampling_Mean Imputati...  
 4    Logistic Regression_Oversampling_Mean Imputati...  
 5    Logistic Regression_Oversampling_Mean Imputati...  
 6    Logistic Regression_Oversampling_Mean Imputati...  
 7    Logistic Regression_Oversampling_Mean Imputati...  
 8    Logistic Regression_Oversampling_Mean Imputati...  
 9    Logistic Regression_Oversampling_Mean Imputati...  
 10   Logistic Regression_Oversampling_Mean Imputati...  
 11   Logistic Regression_Oversampling_Mean Imputati...  
 12   Logistic Regression_Oversampling_Mean Imputati...  
 13   Logistic Regression_Oversampling_Mean Imputati...  
 14   Logistic Regression_Oversampling_Mean Imputati...  
 15   Logistic Regression_Oversampling_Mean Imputati...  
 16   Logistic Regression_Oversampling_Mean Imputati...  
 17   Logistic Regression_Oversampling_Mean Imputati...  
 18   Logistic Regression_Oversampling_Mean Imputati...  
 19   Logistic Regression_Oversampling_Mean Imputati...  
 20   Logistic Regression_Oversampling_Mean Imputati...  
 21   Logistic Regression_Oversampling_Mean Imputati...  
 22   Logistic Regression_Oversampling_Mean Imputati...  
 23   Logistic Regression_Oversampling_Mean Imputati...  
 24   Logistic Regression_Oversampling_Mean Imputati...  
 25   Logistic Regression_Oversampling_Mean Imputati...  
 26   Logistic Regression_Oversampling_Mean Imputati...  
 27   Logistic Regression_Oversampling_Mean Imputati...  
 28   Logistic Regression_Oversampling_Mean Imputati...  
 29   Logistic Regression_Oversampling_Mean Imputati...  
 30   Logistic Regression_Oversampling_Mean Imputati...  
 31   Logistic Regression_Oversampling_Mean Imputati...  
 32   Logistic Regression_Oversampling_Mean Imputati...  
 33   Logistic Regression_Oversampling_Mean Imputati...  
 34   Logistic Regression_Oversampling_Mean Imputati...  
 35   Logistic Regression_Oversampling_Mean Imputati...  
 36   Logistic Regression_Oversampling_Mean Imputati...  
 37   Logistic Regression_Oversampling_Mean Imputati...  
 38   Logistic Regression_Oversampling_Mean Imputati...  
 39   Logistic Regression_Oversampling_Mean Imputati...  
 40   Logistic Regression_Oversampling_Mean Imputati...  
 41   Logistic Regression_Oversampling_Mean Imputati...  
 42   Logistic Regression_Oversampling_Mean Imputati...  
 43   Logistic Regression_Oversampling_Mean Imputati...  
 44   Logistic Regression_Oversampling_Mean Imputati...  
 45   Logistic Regression_Oversampling_Mean Imputati...  
 46   Logistic Regression_Oversampling_Mean Imputati...  
 47   Logistic Regression_Oversampling_Mean Imputati...  
 48   Logistic Regression_Oversampling_Mean Imputati...  
 49   Logistic Regression_Oversampling_Mean Imputati...  
 50   Logistic Regression_Oversampling_Mean Imputati...  
 51   Logistic Regression_Oversampling_Mean Imputati...  
 52   Logistic Regression_Oversampling_Mean Imputati...  
 53   Logistic Regression_Oversampling_Mean Imputati...  
 54   Logistic Regression_Oversampling_Mean Imputati...  
 55   Logistic Regression_Oversampling_Mean Imputati...  
 56   Logistic Regression_Oversampling_Mean Imputati...  
 57   Logistic Regression_Oversampling_Mean Imputati...  
 58   Logistic Regression_Oversampling_Mean Imputati...  
 59   Logistic Regression_Oversampling_Mean Imputati...  
 60   Logistic Regression_Oversampling_Mean Imputati...  
 61   Logistic Regression_Oversampling_Mean Imputati...  
 62   Logistic Regression_Oversampling_Mean Imputati...  
 63   Logistic Regression_Oversampling_Mean Imputati...  
 64   Logistic Regression_Oversampling_Mean Imputati...  
 65   Logistic Regression_Oversampling_Mean Imputati...  
 66   Logistic Regression_Oversampling_Mean Imputati...  
 67   Logistic Regression_Oversampling_Mean Imputati...  
 68   Logistic Regression_Oversampling_Mean Imputati...  
 69   Logistic Regression_Oversampling_Mean Imputati...  
 70   Logistic Regression_Oversampling_Mean Imputati...  
 71   Logistic Regression_Oversampling_Mean Imputati...  
 72   Logistic Regression_Oversampling_Mean Imputati...  
 73   Logistic Regression_Oversampling_Mean Imputati...  
 74   Logistic Regression_Oversampling_Mean Imputati...  
 75   Logistic Regression_Oversampling_Mean Imputati...  
 76   Logistic Regression_Oversampling_Mean Imputati...  
 77   Logistic Regression_Oversampling_Mean Imputati...  
 78   Logistic Regression_Oversampling_Mean Imputati...  
 79   Logistic Regression_Oversampling_Mean Imputati...  
 80   Logistic Regression_Oversampling_Mean Imputati...  
 81   Logistic Regression_Oversampling_Mean Imputati...  
 82   Logistic Regression_Oversampling_Mean Imputati...  
 83   Logistic Regression_Oversampling_Mean Imputati...  
 84   Logistic Regression_Oversampling_Mean Imputati...  
 85   Logistic Regression_Oversampling_Mean Imputati...  
 86   Logistic Regression_Oversampling_Mean Imputati...  
 87   Logistic Regression_Oversampling_Mean Imputati...  
 88   Logistic Regression_Oversampling_Mean Imputati...  
 89   Logistic Regression_Oversampling_Mean Imputati...  
 90   Logistic Regression_Oversampling_Mean Imputati...  
 91   Logistic Regression_Oversampling_Mean Imputati...  
 92   Logistic Regression_Oversampling_Mean Imputati...  
 93   Logistic Regression_Oversampling_Mean Imputati...  
 94   Logistic Regression_Oversampling_Mean Imputati...  
 95   Logistic Regression_Oversampling_Mean Imputati...  
 96   Logistic Regression_Oversampling_Mean Imputati...  
 97   Logistic Regression_Oversampling_Mean Imputati...  
 98   Logistic Regression_Oversampling_Mean Imputati...  
 99   Logistic Regression_Oversampling_Mean Imputati...  
 100  Logistic Regression_Oversampling_Mean Imputati...  
 101  Logistic Regression_Oversampling_Mean Imputati...  
 102  Logistic Regression_Oversampling_Mean Imputati...  
 103  Logistic Regression_Oversampling_Mean Imputati...  
 104  Logistic Regression_Oversampling_Mean Imputati...  
 105  Logistic Regression_Oversampling_Mean Imputati...  
 106  Logistic Regression_Oversampling_Mean Imputati...  
 107  Logistic Regression_Oversampling_Mean Imputati...  
 108  Logistic Regression_Oversampling_Mean Imputati...  
 109  Logistic Regression_Oversampling_Mean Imputati...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.495654             NaN    Median Imputation   
 1    Logistic Regression  0.085884             NaN    Median Imputation   
 2    Logistic Regression  0.734177             NaN    Median Imputation   
 3    Logistic Regression  0.153778             NaN    Median Imputation   
 4    Logistic Regression  0.617012             NaN    Median Imputation   
 5    Logistic Regression  0.231823             NaN    Median Imputation   
 6    Logistic Regression  0.234024             NaN    Median Imputation   
 7    Logistic Regression       NaN             1.0    Median Imputation   
 8    Logistic Regression       NaN            None    Median Imputation   
 9    Logistic Regression       NaN           False    Median Imputation   
 10   Logistic Regression       NaN            True    Median Imputation   
 11   Logistic Regression       NaN               1    Median Imputation   
 12   Logistic Regression       NaN            None    Median Imputation   
 13   Logistic Regression       NaN             100    Median Imputation   
 14   Logistic Regression       NaN            auto    Median Imputation   
 15   Logistic Regression       NaN            None    Median Imputation   
 16   Logistic Regression       NaN              l2    Median Imputation   
 17   Logistic Regression       NaN            None    Median Imputation   
 18   Logistic Regression       NaN           lbfgs    Median Imputation   
 19   Logistic Regression       NaN          0.0001    Median Imputation   
 20   Logistic Regression       NaN               0    Median Imputation   
 21   Logistic Regression       NaN           False    Median Imputation   
 22   Logistic Regression  0.555439             NaN    Median Imputation   
 23   Logistic Regression  0.060554             NaN    Median Imputation   
 24   Logistic Regression  0.640244             NaN    Median Imputation   
 25   Logistic Regression  0.110643             NaN    Median Imputation   
 26   Logistic Regression  0.640560             NaN    Median Imputation   
 27   Logistic Regression  0.239804             NaN    Median Imputation   
 28   Logistic Regression  0.281121             NaN    Median Imputation   
 29   Logistic Regression       NaN             1.0    Median Imputation   
 30   Logistic Regression       NaN            None    Median Imputation   
 31   Logistic Regression       NaN           False    Median Imputation   
 32   Logistic Regression       NaN            True    Median Imputation   
 33   Logistic Regression       NaN               1    Median Imputation   
 34   Logistic Regression       NaN            None    Median Imputation   
 35   Logistic Regression       NaN             100    Median Imputation   
 36   Logistic Regression       NaN            auto    Median Imputation   
 37   Logistic Regression       NaN            None    Median Imputation   
 38   Logistic Regression       NaN              l2    Median Imputation   
 39   Logistic Regression       NaN            None    Median Imputation   
 40   Logistic Regression       NaN           lbfgs    Median Imputation   
 41   Logistic Regression       NaN          0.0001    Median Imputation   
 42   Logistic Regression       NaN               0    Median Imputation   
 43   Logistic Regression       NaN           False    Median Imputation   
 44   Logistic Regression  0.527258             NaN    Median Imputation   
 45   Logistic Regression  0.065875             NaN    Median Imputation   
 46   Logistic Regression  0.652406             NaN    Median Imputation   
 47   Logistic Regression  0.119667             NaN    Median Imputation   
 48   Logistic Regression  0.634617             NaN    Median Imputation   
 49   Logistic Regression  0.217145             NaN    Median Imputation   
 50   Logistic Regression  0.269234             NaN    Median Imputation   
 51   Logistic Regression       NaN             1.0    Median Imputation   
 52   Logistic Regression       NaN            None    Median Imputation   
 53   Logistic Regression       NaN           False    Median Imputation   
 54   Logistic Regression       NaN            True    Median Imputation   
 55   Logistic Regression       NaN               1    Median Imputation   
 56   Logistic Regression       NaN            None    Median Imputation   
 57   Logistic Regression       NaN             100    Median Imputation   
 58   Logistic Regression       NaN            auto    Median Imputation   
 59   Logistic Regression       NaN            None    Median Imputation   
 60   Logistic Regression       NaN              l2    Median Imputation   
 61   Logistic Regression       NaN            None    Median Imputation   
 62   Logistic Regression       NaN           lbfgs    Median Imputation   
 63   Logistic Regression       NaN          0.0001    Median Imputation   
 64   Logistic Regression       NaN               0    Median Imputation   
 65   Logistic Regression       NaN           False    Median Imputation   
 66   Logistic Regression  0.544257             NaN    Median Imputation   
 67   Logistic Regression  0.074834             NaN    Median Imputation   
 68   Logistic Regression  0.688776             NaN    Median Imputation   
 69   Logistic Regression  0.135000             NaN    Median Imputation   
 70   Logistic Regression  0.655620             NaN    Median Imputation   
 71   Logistic Regression  0.251633             NaN    Median Imputation   
 72   Logistic Regression  0.311240             NaN    Median Imputation   
 73   Logistic Regression       NaN             1.0    Median Imputation   
 74   Logistic Regression       NaN            None    Median Imputation   
 75   Logistic Regression       NaN           False    Median Imputation   
 76   Logistic Regression       NaN            True    Median Imputation   
 77   Logistic Regression       NaN               1    Median Imputation   
 78   Logistic Regression       NaN            None    Median Imputation   
 79   Logistic Regression       NaN             100    Median Imputation   
 80   Logistic Regression       NaN            auto    Median Imputation   
 81   Logistic Regression       NaN            None    Median Imputation   
 82   Logistic Regression       NaN              l2    Median Imputation   
 83   Logistic Regression       NaN            None    Median Imputation   
 84   Logistic Regression       NaN           lbfgs    Median Imputation   
 85   Logistic Regression       NaN          0.0001    Median Imputation   
 86   Logistic Regression       NaN               0    Median Imputation   
 87   Logistic Regression       NaN           False    Median Imputation   
 88   Logistic Regression  0.562961             NaN    Median Imputation   
 89   Logistic Regression  0.081739             NaN    Median Imputation   
 90   Logistic Regression  0.652778             NaN    Median Imputation   
 91   Logistic Regression  0.145286             NaN    Median Imputation   
 92   Logistic Regression  0.642832             NaN    Median Imputation   
 93   Logistic Regression  0.239898             NaN    Median Imputation   
 94   Logistic Regression  0.285665             NaN    Median Imputation   
 95   Logistic Regression       NaN             1.0    Median Imputation   
 96   Logistic Regression       NaN            None    Median Imputation   
 97   Logistic Regression       NaN           False    Median Imputation   
 98   Logistic Regression       NaN            True    Median Imputation   
 99   Logistic Regression       NaN               1    Median Imputation   
 100  Logistic Regression       NaN            None    Median Imputation   
 101  Logistic Regression       NaN             100    Median Imputation   
 102  Logistic Regression       NaN            auto    Median Imputation   
 103  Logistic Regression       NaN            None    Median Imputation   
 104  Logistic Regression       NaN              l2    Median Imputation   
 105  Logistic Regression       NaN            None    Median Imputation   
 106  Logistic Regression       NaN           lbfgs    Median Imputation   
 107  Logistic Regression       NaN          0.0001    Median Imputation   
 108  Logistic Regression       NaN               0    Median Imputation   
 109  Logistic Regression       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Oversampling_Median Imputa...  
 1    Logistic Regression_Oversampling_Median Imputa...  
 2    Logistic Regression_Oversampling_Median Imputa...  
 3    Logistic Regression_Oversampling_Median Imputa...  
 4    Logistic Regression_Oversampling_Median Imputa...  
 5    Logistic Regression_Oversampling_Median Imputa...  
 6    Logistic Regression_Oversampling_Median Imputa...  
 7    Logistic Regression_Oversampling_Median Imputa...  
 8    Logistic Regression_Oversampling_Median Imputa...  
 9    Logistic Regression_Oversampling_Median Imputa...  
 10   Logistic Regression_Oversampling_Median Imputa...  
 11   Logistic Regression_Oversampling_Median Imputa...  
 12   Logistic Regression_Oversampling_Median Imputa...  
 13   Logistic Regression_Oversampling_Median Imputa...  
 14   Logistic Regression_Oversampling_Median Imputa...  
 15   Logistic Regression_Oversampling_Median Imputa...  
 16   Logistic Regression_Oversampling_Median Imputa...  
 17   Logistic Regression_Oversampling_Median Imputa...  
 18   Logistic Regression_Oversampling_Median Imputa...  
 19   Logistic Regression_Oversampling_Median Imputa...  
 20   Logistic Regression_Oversampling_Median Imputa...  
 21   Logistic Regression_Oversampling_Median Imputa...  
 22   Logistic Regression_Oversampling_Median Imputa...  
 23   Logistic Regression_Oversampling_Median Imputa...  
 24   Logistic Regression_Oversampling_Median Imputa...  
 25   Logistic Regression_Oversampling_Median Imputa...  
 26   Logistic Regression_Oversampling_Median Imputa...  
 27   Logistic Regression_Oversampling_Median Imputa...  
 28   Logistic Regression_Oversampling_Median Imputa...  
 29   Logistic Regression_Oversampling_Median Imputa...  
 30   Logistic Regression_Oversampling_Median Imputa...  
 31   Logistic Regression_Oversampling_Median Imputa...  
 32   Logistic Regression_Oversampling_Median Imputa...  
 33   Logistic Regression_Oversampling_Median Imputa...  
 34   Logistic Regression_Oversampling_Median Imputa...  
 35   Logistic Regression_Oversampling_Median Imputa...  
 36   Logistic Regression_Oversampling_Median Imputa...  
 37   Logistic Regression_Oversampling_Median Imputa...  
 38   Logistic Regression_Oversampling_Median Imputa...  
 39   Logistic Regression_Oversampling_Median Imputa...  
 40   Logistic Regression_Oversampling_Median Imputa...  
 41   Logistic Regression_Oversampling_Median Imputa...  
 42   Logistic Regression_Oversampling_Median Imputa...  
 43   Logistic Regression_Oversampling_Median Imputa...  
 44   Logistic Regression_Oversampling_Median Imputa...  
 45   Logistic Regression_Oversampling_Median Imputa...  
 46   Logistic Regression_Oversampling_Median Imputa...  
 47   Logistic Regression_Oversampling_Median Imputa...  
 48   Logistic Regression_Oversampling_Median Imputa...  
 49   Logistic Regression_Oversampling_Median Imputa...  
 50   Logistic Regression_Oversampling_Median Imputa...  
 51   Logistic Regression_Oversampling_Median Imputa...  
 52   Logistic Regression_Oversampling_Median Imputa...  
 53   Logistic Regression_Oversampling_Median Imputa...  
 54   Logistic Regression_Oversampling_Median Imputa...  
 55   Logistic Regression_Oversampling_Median Imputa...  
 56   Logistic Regression_Oversampling_Median Imputa...  
 57   Logistic Regression_Oversampling_Median Imputa...  
 58   Logistic Regression_Oversampling_Median Imputa...  
 59   Logistic Regression_Oversampling_Median Imputa...  
 60   Logistic Regression_Oversampling_Median Imputa...  
 61   Logistic Regression_Oversampling_Median Imputa...  
 62   Logistic Regression_Oversampling_Median Imputa...  
 63   Logistic Regression_Oversampling_Median Imputa...  
 64   Logistic Regression_Oversampling_Median Imputa...  
 65   Logistic Regression_Oversampling_Median Imputa...  
 66   Logistic Regression_Oversampling_Median Imputa...  
 67   Logistic Regression_Oversampling_Median Imputa...  
 68   Logistic Regression_Oversampling_Median Imputa...  
 69   Logistic Regression_Oversampling_Median Imputa...  
 70   Logistic Regression_Oversampling_Median Imputa...  
 71   Logistic Regression_Oversampling_Median Imputa...  
 72   Logistic Regression_Oversampling_Median Imputa...  
 73   Logistic Regression_Oversampling_Median Imputa...  
 74   Logistic Regression_Oversampling_Median Imputa...  
 75   Logistic Regression_Oversampling_Median Imputa...  
 76   Logistic Regression_Oversampling_Median Imputa...  
 77   Logistic Regression_Oversampling_Median Imputa...  
 78   Logistic Regression_Oversampling_Median Imputa...  
 79   Logistic Regression_Oversampling_Median Imputa...  
 80   Logistic Regression_Oversampling_Median Imputa...  
 81   Logistic Regression_Oversampling_Median Imputa...  
 82   Logistic Regression_Oversampling_Median Imputa...  
 83   Logistic Regression_Oversampling_Median Imputa...  
 84   Logistic Regression_Oversampling_Median Imputa...  
 85   Logistic Regression_Oversampling_Median Imputa...  
 86   Logistic Regression_Oversampling_Median Imputa...  
 87   Logistic Regression_Oversampling_Median Imputa...  
 88   Logistic Regression_Oversampling_Median Imputa...  
 89   Logistic Regression_Oversampling_Median Imputa...  
 90   Logistic Regression_Oversampling_Median Imputa...  
 91   Logistic Regression_Oversampling_Median Imputa...  
 92   Logistic Regression_Oversampling_Median Imputa...  
 93   Logistic Regression_Oversampling_Median Imputa...  
 94   Logistic Regression_Oversampling_Median Imputa...  
 95   Logistic Regression_Oversampling_Median Imputa...  
 96   Logistic Regression_Oversampling_Median Imputa...  
 97   Logistic Regression_Oversampling_Median Imputa...  
 98   Logistic Regression_Oversampling_Median Imputa...  
 99   Logistic Regression_Oversampling_Median Imputa...  
 100  Logistic Regression_Oversampling_Median Imputa...  
 101  Logistic Regression_Oversampling_Median Imputa...  
 102  Logistic Regression_Oversampling_Median Imputa...  
 103  Logistic Regression_Oversampling_Median Imputa...  
 104  Logistic Regression_Oversampling_Median Imputa...  
 105  Logistic Regression_Oversampling_Median Imputa...  
 106  Logistic Regression_Oversampling_Median Imputa...  
 107  Logistic Regression_Oversampling_Median Imputa...  
 108  Logistic Regression_Oversampling_Median Imputa...  
 109  Logistic Regression_Oversampling_Median Imputa...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.454569             NaN      Zero Imputation   
 1    Logistic Regression  0.082423             NaN      Zero Imputation   
 2    Logistic Regression  0.763713             NaN      Zero Imputation   
 3    Logistic Regression  0.148788             NaN      Zero Imputation   
 4    Logistic Regression  0.620113             NaN      Zero Imputation   
 5    Logistic Regression  0.226164             NaN      Zero Imputation   
 6    Logistic Regression  0.240225             NaN      Zero Imputation   
 7    Logistic Regression       NaN             1.0      Zero Imputation   
 8    Logistic Regression       NaN            None      Zero Imputation   
 9    Logistic Regression       NaN           False      Zero Imputation   
 10   Logistic Regression       NaN            True      Zero Imputation   
 11   Logistic Regression       NaN               1      Zero Imputation   
 12   Logistic Regression       NaN            None      Zero Imputation   
 13   Logistic Regression       NaN             100      Zero Imputation   
 14   Logistic Regression       NaN            auto      Zero Imputation   
 15   Logistic Regression       NaN            None      Zero Imputation   
 16   Logistic Regression       NaN              l2      Zero Imputation   
 17   Logistic Regression       NaN            None      Zero Imputation   
 18   Logistic Regression       NaN           lbfgs      Zero Imputation   
 19   Logistic Regression       NaN          0.0001      Zero Imputation   
 20   Logistic Regression       NaN               0      Zero Imputation   
 21   Logistic Regression       NaN           False      Zero Imputation   
 22   Logistic Regression  0.441138             NaN      Zero Imputation   
 23   Logistic Regression  0.056210             NaN      Zero Imputation   
 24   Logistic Regression  0.756098             NaN      Zero Imputation   
 25   Logistic Regression  0.104641             NaN      Zero Imputation   
 26   Logistic Regression  0.632667             NaN      Zero Imputation   
 27   Logistic Regression  0.211379             NaN      Zero Imputation   
 28   Logistic Regression  0.265334             NaN      Zero Imputation   
 29   Logistic Regression       NaN             1.0      Zero Imputation   
 30   Logistic Regression       NaN            None      Zero Imputation   
 31   Logistic Regression       NaN           False      Zero Imputation   
 32   Logistic Regression       NaN            True      Zero Imputation   
 33   Logistic Regression       NaN               1      Zero Imputation   
 34   Logistic Regression       NaN            None      Zero Imputation   
 35   Logistic Regression       NaN             100      Zero Imputation   
 36   Logistic Regression       NaN            auto      Zero Imputation   
 37   Logistic Regression       NaN            None      Zero Imputation   
 38   Logistic Regression       NaN              l2      Zero Imputation   
 39   Logistic Regression       NaN            None      Zero Imputation   
 40   Logistic Regression       NaN           lbfgs      Zero Imputation   
 41   Logistic Regression       NaN          0.0001      Zero Imputation   
 42   Logistic Regression       NaN               0      Zero Imputation   
 43   Logistic Regression       NaN           False      Zero Imputation   
 44   Logistic Regression  0.430866             NaN      Zero Imputation   
 45   Logistic Regression  0.064431             NaN      Zero Imputation   
 46   Logistic Regression  0.780749             NaN      Zero Imputation   
 47   Logistic Regression  0.119038             NaN      Zero Imputation   
 48   Logistic Regression  0.632760             NaN      Zero Imputation   
 49   Logistic Regression  0.214095             NaN      Zero Imputation   
 50   Logistic Regression  0.265519             NaN      Zero Imputation   
 51   Logistic Regression       NaN             1.0      Zero Imputation   
 52   Logistic Regression       NaN            None      Zero Imputation   
 53   Logistic Regression       NaN           False      Zero Imputation   
 54   Logistic Regression       NaN            True      Zero Imputation   
 55   Logistic Regression       NaN               1      Zero Imputation   
 56   Logistic Regression       NaN            None      Zero Imputation   
 57   Logistic Regression       NaN             100      Zero Imputation   
 58   Logistic Regression       NaN            auto      Zero Imputation   
 59   Logistic Regression       NaN            None      Zero Imputation   
 60   Logistic Regression       NaN              l2      Zero Imputation   
 61   Logistic Regression       NaN            None      Zero Imputation   
 62   Logistic Regression       NaN           lbfgs      Zero Imputation   
 63   Logistic Regression       NaN          0.0001      Zero Imputation   
 64   Logistic Regression       NaN               0      Zero Imputation   
 65   Logistic Regression       NaN           False      Zero Imputation   
 66   Logistic Regression  0.444942             NaN      Zero Imputation   
 67   Logistic Regression  0.067059             NaN      Zero Imputation   
 68   Logistic Regression  0.755102             NaN      Zero Imputation   
 69   Logistic Regression  0.123179             NaN      Zero Imputation   
 70   Logistic Regression  0.642907             NaN      Zero Imputation   
 71   Logistic Regression  0.228339             NaN      Zero Imputation   
 72   Logistic Regression  0.285815             NaN      Zero Imputation   
 73   Logistic Regression       NaN             1.0      Zero Imputation   
 74   Logistic Regression       NaN            None      Zero Imputation   
 75   Logistic Regression       NaN           False      Zero Imputation   
 76   Logistic Regression       NaN            True      Zero Imputation   
 77   Logistic Regression       NaN               1      Zero Imputation   
 78   Logistic Regression       NaN            None      Zero Imputation   
 79   Logistic Regression       NaN             100      Zero Imputation   
 80   Logistic Regression       NaN            auto      Zero Imputation   
 81   Logistic Regression       NaN            None      Zero Imputation   
 82   Logistic Regression       NaN              l2      Zero Imputation   
 83   Logistic Regression       NaN            None      Zero Imputation   
 84   Logistic Regression       NaN           lbfgs      Zero Imputation   
 85   Logistic Regression       NaN          0.0001      Zero Imputation   
 86   Logistic Regression       NaN               0      Zero Imputation   
 87   Logistic Regression       NaN           False      Zero Imputation   
 88   Logistic Regression  0.461538             NaN      Zero Imputation   
 89   Logistic Regression  0.073694             NaN      Zero Imputation   
 90   Logistic Regression  0.731481             NaN      Zero Imputation   
 91   Logistic Regression  0.133898             NaN      Zero Imputation   
 92   Logistic Regression  0.631612             NaN      Zero Imputation   
 93   Logistic Regression  0.217577             NaN      Zero Imputation   
 94   Logistic Regression  0.263224             NaN      Zero Imputation   
 95   Logistic Regression       NaN             1.0      Zero Imputation   
 96   Logistic Regression       NaN            None      Zero Imputation   
 97   Logistic Regression       NaN           False      Zero Imputation   
 98   Logistic Regression       NaN            True      Zero Imputation   
 99   Logistic Regression       NaN               1      Zero Imputation   
 100  Logistic Regression       NaN            None      Zero Imputation   
 101  Logistic Regression       NaN             100      Zero Imputation   
 102  Logistic Regression       NaN            auto      Zero Imputation   
 103  Logistic Regression       NaN            None      Zero Imputation   
 104  Logistic Regression       NaN              l2      Zero Imputation   
 105  Logistic Regression       NaN            None      Zero Imputation   
 106  Logistic Regression       NaN           lbfgs      Zero Imputation   
 107  Logistic Regression       NaN          0.0001      Zero Imputation   
 108  Logistic Regression       NaN               0      Zero Imputation   
 109  Logistic Regression       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Undersampling_Zero Imputat...  
 1    Logistic Regression_Undersampling_Zero Imputat...  
 2    Logistic Regression_Undersampling_Zero Imputat...  
 3    Logistic Regression_Undersampling_Zero Imputat...  
 4    Logistic Regression_Undersampling_Zero Imputat...  
 5    Logistic Regression_Undersampling_Zero Imputat...  
 6    Logistic Regression_Undersampling_Zero Imputat...  
 7    Logistic Regression_Undersampling_Zero Imputat...  
 8    Logistic Regression_Undersampling_Zero Imputat...  
 9    Logistic Regression_Undersampling_Zero Imputat...  
 10   Logistic Regression_Undersampling_Zero Imputat...  
 11   Logistic Regression_Undersampling_Zero Imputat...  
 12   Logistic Regression_Undersampling_Zero Imputat...  
 13   Logistic Regression_Undersampling_Zero Imputat...  
 14   Logistic Regression_Undersampling_Zero Imputat...  
 15   Logistic Regression_Undersampling_Zero Imputat...  
 16   Logistic Regression_Undersampling_Zero Imputat...  
 17   Logistic Regression_Undersampling_Zero Imputat...  
 18   Logistic Regression_Undersampling_Zero Imputat...  
 19   Logistic Regression_Undersampling_Zero Imputat...  
 20   Logistic Regression_Undersampling_Zero Imputat...  
 21   Logistic Regression_Undersampling_Zero Imputat...  
 22   Logistic Regression_Undersampling_Zero Imputat...  
 23   Logistic Regression_Undersampling_Zero Imputat...  
 24   Logistic Regression_Undersampling_Zero Imputat...  
 25   Logistic Regression_Undersampling_Zero Imputat...  
 26   Logistic Regression_Undersampling_Zero Imputat...  
 27   Logistic Regression_Undersampling_Zero Imputat...  
 28   Logistic Regression_Undersampling_Zero Imputat...  
 29   Logistic Regression_Undersampling_Zero Imputat...  
 30   Logistic Regression_Undersampling_Zero Imputat...  
 31   Logistic Regression_Undersampling_Zero Imputat...  
 32   Logistic Regression_Undersampling_Zero Imputat...  
 33   Logistic Regression_Undersampling_Zero Imputat...  
 34   Logistic Regression_Undersampling_Zero Imputat...  
 35   Logistic Regression_Undersampling_Zero Imputat...  
 36   Logistic Regression_Undersampling_Zero Imputat...  
 37   Logistic Regression_Undersampling_Zero Imputat...  
 38   Logistic Regression_Undersampling_Zero Imputat...  
 39   Logistic Regression_Undersampling_Zero Imputat...  
 40   Logistic Regression_Undersampling_Zero Imputat...  
 41   Logistic Regression_Undersampling_Zero Imputat...  
 42   Logistic Regression_Undersampling_Zero Imputat...  
 43   Logistic Regression_Undersampling_Zero Imputat...  
 44   Logistic Regression_Undersampling_Zero Imputat...  
 45   Logistic Regression_Undersampling_Zero Imputat...  
 46   Logistic Regression_Undersampling_Zero Imputat...  
 47   Logistic Regression_Undersampling_Zero Imputat...  
 48   Logistic Regression_Undersampling_Zero Imputat...  
 49   Logistic Regression_Undersampling_Zero Imputat...  
 50   Logistic Regression_Undersampling_Zero Imputat...  
 51   Logistic Regression_Undersampling_Zero Imputat...  
 52   Logistic Regression_Undersampling_Zero Imputat...  
 53   Logistic Regression_Undersampling_Zero Imputat...  
 54   Logistic Regression_Undersampling_Zero Imputat...  
 55   Logistic Regression_Undersampling_Zero Imputat...  
 56   Logistic Regression_Undersampling_Zero Imputat...  
 57   Logistic Regression_Undersampling_Zero Imputat...  
 58   Logistic Regression_Undersampling_Zero Imputat...  
 59   Logistic Regression_Undersampling_Zero Imputat...  
 60   Logistic Regression_Undersampling_Zero Imputat...  
 61   Logistic Regression_Undersampling_Zero Imputat...  
 62   Logistic Regression_Undersampling_Zero Imputat...  
 63   Logistic Regression_Undersampling_Zero Imputat...  
 64   Logistic Regression_Undersampling_Zero Imputat...  
 65   Logistic Regression_Undersampling_Zero Imputat...  
 66   Logistic Regression_Undersampling_Zero Imputat...  
 67   Logistic Regression_Undersampling_Zero Imputat...  
 68   Logistic Regression_Undersampling_Zero Imputat...  
 69   Logistic Regression_Undersampling_Zero Imputat...  
 70   Logistic Regression_Undersampling_Zero Imputat...  
 71   Logistic Regression_Undersampling_Zero Imputat...  
 72   Logistic Regression_Undersampling_Zero Imputat...  
 73   Logistic Regression_Undersampling_Zero Imputat...  
 74   Logistic Regression_Undersampling_Zero Imputat...  
 75   Logistic Regression_Undersampling_Zero Imputat...  
 76   Logistic Regression_Undersampling_Zero Imputat...  
 77   Logistic Regression_Undersampling_Zero Imputat...  
 78   Logistic Regression_Undersampling_Zero Imputat...  
 79   Logistic Regression_Undersampling_Zero Imputat...  
 80   Logistic Regression_Undersampling_Zero Imputat...  
 81   Logistic Regression_Undersampling_Zero Imputat...  
 82   Logistic Regression_Undersampling_Zero Imputat...  
 83   Logistic Regression_Undersampling_Zero Imputat...  
 84   Logistic Regression_Undersampling_Zero Imputat...  
 85   Logistic Regression_Undersampling_Zero Imputat...  
 86   Logistic Regression_Undersampling_Zero Imputat...  
 87   Logistic Regression_Undersampling_Zero Imputat...  
 88   Logistic Regression_Undersampling_Zero Imputat...  
 89   Logistic Regression_Undersampling_Zero Imputat...  
 90   Logistic Regression_Undersampling_Zero Imputat...  
 91   Logistic Regression_Undersampling_Zero Imputat...  
 92   Logistic Regression_Undersampling_Zero Imputat...  
 93   Logistic Regression_Undersampling_Zero Imputat...  
 94   Logistic Regression_Undersampling_Zero Imputat...  
 95   Logistic Regression_Undersampling_Zero Imputat...  
 96   Logistic Regression_Undersampling_Zero Imputat...  
 97   Logistic Regression_Undersampling_Zero Imputat...  
 98   Logistic Regression_Undersampling_Zero Imputat...  
 99   Logistic Regression_Undersampling_Zero Imputat...  
 100  Logistic Regression_Undersampling_Zero Imputat...  
 101  Logistic Regression_Undersampling_Zero Imputat...  
 102  Logistic Regression_Undersampling_Zero Imputat...  
 103  Logistic Regression_Undersampling_Zero Imputat...  
 104  Logistic Regression_Undersampling_Zero Imputat...  
 105  Logistic Regression_Undersampling_Zero Imputat...  
 106  Logistic Regression_Undersampling_Zero Imputat...  
 107  Logistic Regression_Undersampling_Zero Imputat...  
 108  Logistic Regression_Undersampling_Zero Imputat...  
 109  Logistic Regression_Undersampling_Zero Imputat...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.430076             NaN      Mean Imputation   
 1    Logistic Regression  0.083801             NaN      Mean Imputation   
 2    Logistic Regression  0.818565             NaN      Mean Imputation   
 3    Logistic Regression  0.152038             NaN      Mean Imputation   
 4    Logistic Regression  0.615221             NaN      Mean Imputation   
 5    Logistic Regression  0.254437             NaN      Mean Imputation   
 6    Logistic Regression  0.230443             NaN      Mean Imputation   
 7    Logistic Regression       NaN             1.0      Mean Imputation   
 8    Logistic Regression       NaN            None      Mean Imputation   
 9    Logistic Regression       NaN           False      Mean Imputation   
 10   Logistic Regression       NaN            True      Mean Imputation   
 11   Logistic Regression       NaN               1      Mean Imputation   
 12   Logistic Regression       NaN            None      Mean Imputation   
 13   Logistic Regression       NaN             100      Mean Imputation   
 14   Logistic Regression       NaN            auto      Mean Imputation   
 15   Logistic Regression       NaN            None      Mean Imputation   
 16   Logistic Regression       NaN              l2      Mean Imputation   
 17   Logistic Regression       NaN            None      Mean Imputation   
 18   Logistic Regression       NaN           lbfgs      Mean Imputation   
 19   Logistic Regression       NaN          0.0001      Mean Imputation   
 20   Logistic Regression       NaN               0      Mean Imputation   
 21   Logistic Regression       NaN           False      Mean Imputation   
 22   Logistic Regression  0.454306             NaN      Mean Imputation   
 23   Logistic Regression  0.058742             NaN      Mean Imputation   
 24   Logistic Regression  0.774390             NaN      Mean Imputation   
 25   Logistic Regression  0.109200             NaN      Mean Imputation   
 26   Logistic Regression  0.601274             NaN      Mean Imputation   
 27   Logistic Regression  0.248276             NaN      Mean Imputation   
 28   Logistic Regression  0.202549             NaN      Mean Imputation   
 29   Logistic Regression       NaN             1.0      Mean Imputation   
 30   Logistic Regression       NaN            None      Mean Imputation   
 31   Logistic Regression       NaN           False      Mean Imputation   
 32   Logistic Regression       NaN            True      Mean Imputation   
 33   Logistic Regression       NaN               1      Mean Imputation   
 34   Logistic Regression       NaN            None      Mean Imputation   
 35   Logistic Regression       NaN             100      Mean Imputation   
 36   Logistic Regression       NaN            auto      Mean Imputation   
 37   Logistic Regression       NaN            None      Mean Imputation   
 38   Logistic Regression       NaN              l2      Mean Imputation   
 39   Logistic Regression       NaN            None      Mean Imputation   
 40   Logistic Regression       NaN           lbfgs      Mean Imputation   
 41   Logistic Regression       NaN          0.0001      Mean Imputation   
 42   Logistic Regression       NaN               0      Mean Imputation   
 43   Logistic Regression       NaN           False      Mean Imputation   
 44   Logistic Regression  0.451409             NaN      Mean Imputation   
 45   Logistic Regression  0.065934             NaN      Mean Imputation   
 46   Logistic Regression  0.770053             NaN      Mean Imputation   
 47   Logistic Regression  0.121468             NaN      Mean Imputation   
 48   Logistic Regression  0.632651             NaN      Mean Imputation   
 49   Logistic Regression  0.264656             NaN      Mean Imputation   
 50   Logistic Regression  0.265303             NaN      Mean Imputation   
 51   Logistic Regression       NaN             1.0      Mean Imputation   
 52   Logistic Regression       NaN            None      Mean Imputation   
 53   Logistic Regression       NaN           False      Mean Imputation   
 54   Logistic Regression       NaN            True      Mean Imputation   
 55   Logistic Regression       NaN               1      Mean Imputation   
 56   Logistic Regression       NaN            None      Mean Imputation   
 57   Logistic Regression       NaN             100      Mean Imputation   
 58   Logistic Regression       NaN            auto      Mean Imputation   
 59   Logistic Regression       NaN            None      Mean Imputation   
 60   Logistic Regression       NaN              l2      Mean Imputation   
 61   Logistic Regression       NaN            None      Mean Imputation   
 62   Logistic Regression       NaN           lbfgs      Mean Imputation   
 63   Logistic Regression       NaN          0.0001      Mean Imputation   
 64   Logistic Regression       NaN               0      Mean Imputation   
 65   Logistic Regression       NaN           False      Mean Imputation   
 66   Logistic Regression  0.464963             NaN      Mean Imputation   
 67   Logistic Regression  0.064960             NaN      Mean Imputation   
 68   Logistic Regression  0.698980             NaN      Mean Imputation   
 69   Logistic Regression  0.118872             NaN      Mean Imputation   
 70   Logistic Regression  0.596278             NaN      Mean Imputation   
 71   Logistic Regression  0.198617             NaN      Mean Imputation   
 72   Logistic Regression  0.192555             NaN      Mean Imputation   
 73   Logistic Regression       NaN             1.0      Mean Imputation   
 74   Logistic Regression       NaN            None      Mean Imputation   
 75   Logistic Regression       NaN           False      Mean Imputation   
 76   Logistic Regression       NaN            True      Mean Imputation   
 77   Logistic Regression       NaN               1      Mean Imputation   
 78   Logistic Regression       NaN            None      Mean Imputation   
 79   Logistic Regression       NaN             100      Mean Imputation   
 80   Logistic Regression       NaN            auto      Mean Imputation   
 81   Logistic Regression       NaN            None      Mean Imputation   
 82   Logistic Regression       NaN              l2      Mean Imputation   
 83   Logistic Regression       NaN            None      Mean Imputation   
 84   Logistic Regression       NaN           lbfgs      Mean Imputation   
 85   Logistic Regression       NaN          0.0001      Mean Imputation   
 86   Logistic Regression       NaN               0      Mean Imputation   
 87   Logistic Regression       NaN           False      Mean Imputation   
 88   Logistic Regression  0.485774             NaN      Mean Imputation   
 89   Logistic Regression  0.071570             NaN      Mean Imputation   
 90   Logistic Regression  0.671296             NaN      Mean Imputation   
 91   Logistic Regression  0.129349             NaN      Mean Imputation   
 92   Logistic Regression  0.567091             NaN      Mean Imputation   
 93   Logistic Regression  0.167194             NaN      Mean Imputation   
 94   Logistic Regression  0.134183             NaN      Mean Imputation   
 95   Logistic Regression       NaN             1.0      Mean Imputation   
 96   Logistic Regression       NaN            None      Mean Imputation   
 97   Logistic Regression       NaN           False      Mean Imputation   
 98   Logistic Regression       NaN            True      Mean Imputation   
 99   Logistic Regression       NaN               1      Mean Imputation   
 100  Logistic Regression       NaN            None      Mean Imputation   
 101  Logistic Regression       NaN             100      Mean Imputation   
 102  Logistic Regression       NaN            auto      Mean Imputation   
 103  Logistic Regression       NaN            None      Mean Imputation   
 104  Logistic Regression       NaN              l2      Mean Imputation   
 105  Logistic Regression       NaN            None      Mean Imputation   
 106  Logistic Regression       NaN           lbfgs      Mean Imputation   
 107  Logistic Regression       NaN          0.0001      Mean Imputation   
 108  Logistic Regression       NaN               0      Mean Imputation   
 109  Logistic Regression       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Undersampling_Mean Imputat...  
 1    Logistic Regression_Undersampling_Mean Imputat...  
 2    Logistic Regression_Undersampling_Mean Imputat...  
 3    Logistic Regression_Undersampling_Mean Imputat...  
 4    Logistic Regression_Undersampling_Mean Imputat...  
 5    Logistic Regression_Undersampling_Mean Imputat...  
 6    Logistic Regression_Undersampling_Mean Imputat...  
 7    Logistic Regression_Undersampling_Mean Imputat...  
 8    Logistic Regression_Undersampling_Mean Imputat...  
 9    Logistic Regression_Undersampling_Mean Imputat...  
 10   Logistic Regression_Undersampling_Mean Imputat...  
 11   Logistic Regression_Undersampling_Mean Imputat...  
 12   Logistic Regression_Undersampling_Mean Imputat...  
 13   Logistic Regression_Undersampling_Mean Imputat...  
 14   Logistic Regression_Undersampling_Mean Imputat...  
 15   Logistic Regression_Undersampling_Mean Imputat...  
 16   Logistic Regression_Undersampling_Mean Imputat...  
 17   Logistic Regression_Undersampling_Mean Imputat...  
 18   Logistic Regression_Undersampling_Mean Imputat...  
 19   Logistic Regression_Undersampling_Mean Imputat...  
 20   Logistic Regression_Undersampling_Mean Imputat...  
 21   Logistic Regression_Undersampling_Mean Imputat...  
 22   Logistic Regression_Undersampling_Mean Imputat...  
 23   Logistic Regression_Undersampling_Mean Imputat...  
 24   Logistic Regression_Undersampling_Mean Imputat...  
 25   Logistic Regression_Undersampling_Mean Imputat...  
 26   Logistic Regression_Undersampling_Mean Imputat...  
 27   Logistic Regression_Undersampling_Mean Imputat...  
 28   Logistic Regression_Undersampling_Mean Imputat...  
 29   Logistic Regression_Undersampling_Mean Imputat...  
 30   Logistic Regression_Undersampling_Mean Imputat...  
 31   Logistic Regression_Undersampling_Mean Imputat...  
 32   Logistic Regression_Undersampling_Mean Imputat...  
 33   Logistic Regression_Undersampling_Mean Imputat...  
 34   Logistic Regression_Undersampling_Mean Imputat...  
 35   Logistic Regression_Undersampling_Mean Imputat...  
 36   Logistic Regression_Undersampling_Mean Imputat...  
 37   Logistic Regression_Undersampling_Mean Imputat...  
 38   Logistic Regression_Undersampling_Mean Imputat...  
 39   Logistic Regression_Undersampling_Mean Imputat...  
 40   Logistic Regression_Undersampling_Mean Imputat...  
 41   Logistic Regression_Undersampling_Mean Imputat...  
 42   Logistic Regression_Undersampling_Mean Imputat...  
 43   Logistic Regression_Undersampling_Mean Imputat...  
 44   Logistic Regression_Undersampling_Mean Imputat...  
 45   Logistic Regression_Undersampling_Mean Imputat...  
 46   Logistic Regression_Undersampling_Mean Imputat...  
 47   Logistic Regression_Undersampling_Mean Imputat...  
 48   Logistic Regression_Undersampling_Mean Imputat...  
 49   Logistic Regression_Undersampling_Mean Imputat...  
 50   Logistic Regression_Undersampling_Mean Imputat...  
 51   Logistic Regression_Undersampling_Mean Imputat...  
 52   Logistic Regression_Undersampling_Mean Imputat...  
 53   Logistic Regression_Undersampling_Mean Imputat...  
 54   Logistic Regression_Undersampling_Mean Imputat...  
 55   Logistic Regression_Undersampling_Mean Imputat...  
 56   Logistic Regression_Undersampling_Mean Imputat...  
 57   Logistic Regression_Undersampling_Mean Imputat...  
 58   Logistic Regression_Undersampling_Mean Imputat...  
 59   Logistic Regression_Undersampling_Mean Imputat...  
 60   Logistic Regression_Undersampling_Mean Imputat...  
 61   Logistic Regression_Undersampling_Mean Imputat...  
 62   Logistic Regression_Undersampling_Mean Imputat...  
 63   Logistic Regression_Undersampling_Mean Imputat...  
 64   Logistic Regression_Undersampling_Mean Imputat...  
 65   Logistic Regression_Undersampling_Mean Imputat...  
 66   Logistic Regression_Undersampling_Mean Imputat...  
 67   Logistic Regression_Undersampling_Mean Imputat...  
 68   Logistic Regression_Undersampling_Mean Imputat...  
 69   Logistic Regression_Undersampling_Mean Imputat...  
 70   Logistic Regression_Undersampling_Mean Imputat...  
 71   Logistic Regression_Undersampling_Mean Imputat...  
 72   Logistic Regression_Undersampling_Mean Imputat...  
 73   Logistic Regression_Undersampling_Mean Imputat...  
 74   Logistic Regression_Undersampling_Mean Imputat...  
 75   Logistic Regression_Undersampling_Mean Imputat...  
 76   Logistic Regression_Undersampling_Mean Imputat...  
 77   Logistic Regression_Undersampling_Mean Imputat...  
 78   Logistic Regression_Undersampling_Mean Imputat...  
 79   Logistic Regression_Undersampling_Mean Imputat...  
 80   Logistic Regression_Undersampling_Mean Imputat...  
 81   Logistic Regression_Undersampling_Mean Imputat...  
 82   Logistic Regression_Undersampling_Mean Imputat...  
 83   Logistic Regression_Undersampling_Mean Imputat...  
 84   Logistic Regression_Undersampling_Mean Imputat...  
 85   Logistic Regression_Undersampling_Mean Imputat...  
 86   Logistic Regression_Undersampling_Mean Imputat...  
 87   Logistic Regression_Undersampling_Mean Imputat...  
 88   Logistic Regression_Undersampling_Mean Imputat...  
 89   Logistic Regression_Undersampling_Mean Imputat...  
 90   Logistic Regression_Undersampling_Mean Imputat...  
 91   Logistic Regression_Undersampling_Mean Imputat...  
 92   Logistic Regression_Undersampling_Mean Imputat...  
 93   Logistic Regression_Undersampling_Mean Imputat...  
 94   Logistic Regression_Undersampling_Mean Imputat...  
 95   Logistic Regression_Undersampling_Mean Imputat...  
 96   Logistic Regression_Undersampling_Mean Imputat...  
 97   Logistic Regression_Undersampling_Mean Imputat...  
 98   Logistic Regression_Undersampling_Mean Imputat...  
 99   Logistic Regression_Undersampling_Mean Imputat...  
 100  Logistic Regression_Undersampling_Mean Imputat...  
 101  Logistic Regression_Undersampling_Mean Imputat...  
 102  Logistic Regression_Undersampling_Mean Imputat...  
 103  Logistic Regression_Undersampling_Mean Imputat...  
 104  Logistic Regression_Undersampling_Mean Imputat...  
 105  Logistic Regression_Undersampling_Mean Imputat...  
 106  Logistic Regression_Undersampling_Mean Imputat...  
 107  Logistic Regression_Undersampling_Mean Imputat...  
 108  Logistic Regression_Undersampling_Mean Imputat...  
 109  Logistic Regression_Undersampling_Mean Imputat...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.486700             NaN    Median Imputation   
 1    Logistic Regression  0.085271             NaN    Median Imputation   
 2    Logistic Regression  0.742616             NaN    Median Imputation   
 3    Logistic Regression  0.152977             NaN    Median Imputation   
 4    Logistic Regression  0.623277             NaN    Median Imputation   
 5    Logistic Regression  0.234272             NaN    Median Imputation   
 6    Logistic Regression  0.246555             NaN    Median Imputation   
 7    Logistic Regression       NaN             1.0    Median Imputation   
 8    Logistic Regression       NaN            None    Median Imputation   
 9    Logistic Regression       NaN           False    Median Imputation   
 10   Logistic Regression       NaN            True    Median Imputation   
 11   Logistic Regression       NaN               1    Median Imputation   
 12   Logistic Regression       NaN            None    Median Imputation   
 13   Logistic Regression       NaN             100    Median Imputation   
 14   Logistic Regression       NaN            auto    Median Imputation   
 15   Logistic Regression       NaN            None    Median Imputation   
 16   Logistic Regression       NaN              l2    Median Imputation   
 17   Logistic Regression       NaN            None    Median Imputation   
 18   Logistic Regression       NaN           lbfgs    Median Imputation   
 19   Logistic Regression       NaN          0.0001    Median Imputation   
 20   Logistic Regression       NaN               0    Median Imputation   
 21   Logistic Regression       NaN           False    Median Imputation   
 22   Logistic Regression  0.561233             NaN    Median Imputation   
 23   Logistic Regression  0.056671             NaN    Median Imputation   
 24   Logistic Regression  0.585366             NaN    Median Imputation   
 25   Logistic Regression  0.103337             NaN    Median Imputation   
 26   Logistic Regression  0.600737             NaN    Median Imputation   
 27   Logistic Regression  0.196864             NaN    Median Imputation   
 28   Logistic Regression  0.201475             NaN    Median Imputation   
 29   Logistic Regression       NaN             1.0    Median Imputation   
 30   Logistic Regression       NaN            None    Median Imputation   
 31   Logistic Regression       NaN           False    Median Imputation   
 32   Logistic Regression       NaN            True    Median Imputation   
 33   Logistic Regression       NaN               1    Median Imputation   
 34   Logistic Regression       NaN            None    Median Imputation   
 35   Logistic Regression       NaN             100    Median Imputation   
 36   Logistic Regression       NaN            auto    Median Imputation   
 37   Logistic Regression       NaN            None    Median Imputation   
 38   Logistic Regression       NaN              l2    Median Imputation   
 39   Logistic Regression       NaN            None    Median Imputation   
 40   Logistic Regression       NaN           lbfgs    Median Imputation   
 41   Logistic Regression       NaN          0.0001    Median Imputation   
 42   Logistic Regression       NaN               0    Median Imputation   
 43   Logistic Regression       NaN           False    Median Imputation   
 44   Logistic Regression  0.568080             NaN    Median Imputation   
 45   Logistic Regression  0.072396             NaN    Median Imputation   
 46   Logistic Regression  0.657754             NaN    Median Imputation   
 47   Logistic Regression  0.130435             NaN    Median Imputation   
 48   Logistic Regression  0.630588             NaN    Median Imputation   
 49   Logistic Regression  0.223405             NaN    Median Imputation   
 50   Logistic Regression  0.261176             NaN    Median Imputation   
 51   Logistic Regression       NaN             1.0    Median Imputation   
 52   Logistic Regression       NaN            None    Median Imputation   
 53   Logistic Regression       NaN           False    Median Imputation   
 54   Logistic Regression       NaN            True    Median Imputation   
 55   Logistic Regression       NaN               1    Median Imputation   
 56   Logistic Regression       NaN            None    Median Imputation   
 57   Logistic Regression       NaN             100    Median Imputation   
 58   Logistic Regression       NaN            auto    Median Imputation   
 59   Logistic Regression       NaN            None    Median Imputation   
 60   Logistic Regression       NaN              l2    Median Imputation   
 61   Logistic Regression       NaN            None    Median Imputation   
 62   Logistic Regression       NaN           lbfgs    Median Imputation   
 63   Logistic Regression       NaN          0.0001    Median Imputation   
 64   Logistic Regression       NaN               0    Median Imputation   
 65   Logistic Regression       NaN           False    Median Imputation   
 66   Logistic Regression  0.490253             NaN    Median Imputation   
 67   Logistic Regression  0.068486             NaN    Median Imputation   
 68   Logistic Regression  0.704082             NaN    Median Imputation   
 69   Logistic Regression  0.124830             NaN    Median Imputation   
 70   Logistic Regression  0.620117             NaN    Median Imputation   
 71   Logistic Regression  0.218464             NaN    Median Imputation   
 72   Logistic Regression  0.240234             NaN    Median Imputation   
 73   Logistic Regression       NaN             1.0    Median Imputation   
 74   Logistic Regression       NaN            None    Median Imputation   
 75   Logistic Regression       NaN           False    Median Imputation   
 76   Logistic Regression       NaN            True    Median Imputation   
 77   Logistic Regression       NaN               1    Median Imputation   
 78   Logistic Regression       NaN            None    Median Imputation   
 79   Logistic Regression       NaN             100    Median Imputation   
 80   Logistic Regression       NaN            auto    Median Imputation   
 81   Logistic Regression       NaN            None    Median Imputation   
 82   Logistic Regression       NaN              l2    Median Imputation   
 83   Logistic Regression       NaN            None    Median Imputation   
 84   Logistic Regression       NaN           lbfgs    Median Imputation   
 85   Logistic Regression       NaN          0.0001    Median Imputation   
 86   Logistic Regression       NaN               0    Median Imputation   
 87   Logistic Regression       NaN           False    Median Imputation   
 88   Logistic Regression  0.532139             NaN    Median Imputation   
 89   Logistic Regression  0.077922             NaN    Median Imputation   
 90   Logistic Regression  0.666667             NaN    Median Imputation   
 91   Logistic Regression  0.139535             NaN    Median Imputation   
 92   Logistic Regression  0.600089             NaN    Median Imputation   
 93   Logistic Regression  0.190689             NaN    Median Imputation   
 94   Logistic Regression  0.200178             NaN    Median Imputation   
 95   Logistic Regression       NaN             1.0    Median Imputation   
 96   Logistic Regression       NaN            None    Median Imputation   
 97   Logistic Regression       NaN           False    Median Imputation   
 98   Logistic Regression       NaN            True    Median Imputation   
 99   Logistic Regression       NaN               1    Median Imputation   
 100  Logistic Regression       NaN            None    Median Imputation   
 101  Logistic Regression       NaN             100    Median Imputation   
 102  Logistic Regression       NaN            auto    Median Imputation   
 103  Logistic Regression       NaN            None    Median Imputation   
 104  Logistic Regression       NaN              l2    Median Imputation   
 105  Logistic Regression       NaN            None    Median Imputation   
 106  Logistic Regression       NaN           lbfgs    Median Imputation   
 107  Logistic Regression       NaN          0.0001    Median Imputation   
 108  Logistic Regression       NaN               0    Median Imputation   
 109  Logistic Regression       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 
                                      Model Unique Code  
 0    Logistic Regression_Undersampling_Median Imput...  
 1    Logistic Regression_Undersampling_Median Imput...  
 2    Logistic Regression_Undersampling_Median Imput...  
 3    Logistic Regression_Undersampling_Median Imput...  
 4    Logistic Regression_Undersampling_Median Imput...  
 5    Logistic Regression_Undersampling_Median Imput...  
 6    Logistic Regression_Undersampling_Median Imput...  
 7    Logistic Regression_Undersampling_Median Imput...  
 8    Logistic Regression_Undersampling_Median Imput...  
 9    Logistic Regression_Undersampling_Median Imput...  
 10   Logistic Regression_Undersampling_Median Imput...  
 11   Logistic Regression_Undersampling_Median Imput...  
 12   Logistic Regression_Undersampling_Median Imput...  
 13   Logistic Regression_Undersampling_Median Imput...  
 14   Logistic Regression_Undersampling_Median Imput...  
 15   Logistic Regression_Undersampling_Median Imput...  
 16   Logistic Regression_Undersampling_Median Imput...  
 17   Logistic Regression_Undersampling_Median Imput...  
 18   Logistic Regression_Undersampling_Median Imput...  
 19   Logistic Regression_Undersampling_Median Imput...  
 20   Logistic Regression_Undersampling_Median Imput...  
 21   Logistic Regression_Undersampling_Median Imput...  
 22   Logistic Regression_Undersampling_Median Imput...  
 23   Logistic Regression_Undersampling_Median Imput...  
 24   Logistic Regression_Undersampling_Median Imput...  
 25   Logistic Regression_Undersampling_Median Imput...  
 26   Logistic Regression_Undersampling_Median Imput...  
 27   Logistic Regression_Undersampling_Median Imput...  
 28   Logistic Regression_Undersampling_Median Imput...  
 29   Logistic Regression_Undersampling_Median Imput...  
 30   Logistic Regression_Undersampling_Median Imput...  
 31   Logistic Regression_Undersampling_Median Imput...  
 32   Logistic Regression_Undersampling_Median Imput...  
 33   Logistic Regression_Undersampling_Median Imput...  
 34   Logistic Regression_Undersampling_Median Imput...  
 35   Logistic Regression_Undersampling_Median Imput...  
 36   Logistic Regression_Undersampling_Median Imput...  
 37   Logistic Regression_Undersampling_Median Imput...  
 38   Logistic Regression_Undersampling_Median Imput...  
 39   Logistic Regression_Undersampling_Median Imput...  
 40   Logistic Regression_Undersampling_Median Imput...  
 41   Logistic Regression_Undersampling_Median Imput...  
 42   Logistic Regression_Undersampling_Median Imput...  
 43   Logistic Regression_Undersampling_Median Imput...  
 44   Logistic Regression_Undersampling_Median Imput...  
 45   Logistic Regression_Undersampling_Median Imput...  
 46   Logistic Regression_Undersampling_Median Imput...  
 47   Logistic Regression_Undersampling_Median Imput...  
 48   Logistic Regression_Undersampling_Median Imput...  
 49   Logistic Regression_Undersampling_Median Imput...  
 50   Logistic Regression_Undersampling_Median Imput...  
 51   Logistic Regression_Undersampling_Median Imput...  
 52   Logistic Regression_Undersampling_Median Imput...  
 53   Logistic Regression_Undersampling_Median Imput...  
 54   Logistic Regression_Undersampling_Median Imput...  
 55   Logistic Regression_Undersampling_Median Imput...  
 56   Logistic Regression_Undersampling_Median Imput...  
 57   Logistic Regression_Undersampling_Median Imput...  
 58   Logistic Regression_Undersampling_Median Imput...  
 59   Logistic Regression_Undersampling_Median Imput...  
 60   Logistic Regression_Undersampling_Median Imput...  
 61   Logistic Regression_Undersampling_Median Imput...  
 62   Logistic Regression_Undersampling_Median Imput...  
 63   Logistic Regression_Undersampling_Median Imput...  
 64   Logistic Regression_Undersampling_Median Imput...  
 65   Logistic Regression_Undersampling_Median Imput...  
 66   Logistic Regression_Undersampling_Median Imput...  
 67   Logistic Regression_Undersampling_Median Imput...  
 68   Logistic Regression_Undersampling_Median Imput...  
 69   Logistic Regression_Undersampling_Median Imput...  
 70   Logistic Regression_Undersampling_Median Imput...  
 71   Logistic Regression_Undersampling_Median Imput...  
 72   Logistic Regression_Undersampling_Median Imput...  
 73   Logistic Regression_Undersampling_Median Imput...  
 74   Logistic Regression_Undersampling_Median Imput...  
 75   Logistic Regression_Undersampling_Median Imput...  
 76   Logistic Regression_Undersampling_Median Imput...  
 77   Logistic Regression_Undersampling_Median Imput...  
 78   Logistic Regression_Undersampling_Median Imput...  
 79   Logistic Regression_Undersampling_Median Imput...  
 80   Logistic Regression_Undersampling_Median Imput...  
 81   Logistic Regression_Undersampling_Median Imput...  
 82   Logistic Regression_Undersampling_Median Imput...  
 83   Logistic Regression_Undersampling_Median Imput...  
 84   Logistic Regression_Undersampling_Median Imput...  
 85   Logistic Regression_Undersampling_Median Imput...  
 86   Logistic Regression_Undersampling_Median Imput...  
 87   Logistic Regression_Undersampling_Median Imput...  
 88   Logistic Regression_Undersampling_Median Imput...  
 89   Logistic Regression_Undersampling_Median Imput...  
 90   Logistic Regression_Undersampling_Median Imput...  
 91   Logistic Regression_Undersampling_Median Imput...  
 92   Logistic Regression_Undersampling_Median Imput...  
 93   Logistic Regression_Undersampling_Median Imput...  
 94   Logistic Regression_Undersampling_Median Imput...  
 95   Logistic Regression_Undersampling_Median Imput...  
 96   Logistic Regression_Undersampling_Median Imput...  
 97   Logistic Regression_Undersampling_Median Imput...  
 98   Logistic Regression_Undersampling_Median Imput...  
 99   Logistic Regression_Undersampling_Median Imput...  
 100  Logistic Regression_Undersampling_Median Imput...  
 101  Logistic Regression_Undersampling_Median Imput...  
 102  Logistic Regression_Undersampling_Median Imput...  
 103  Logistic Regression_Undersampling_Median Imput...  
 104  Logistic Regression_Undersampling_Median Imput...  
 105  Logistic Regression_Undersampling_Median Imput...  
 106  Logistic Regression_Undersampling_Median Imput...  
 107  Logistic Regression_Undersampling_Median Imput...  
 108  Logistic Regression_Undersampling_Median Imput...  
 109  Logistic Regression_Undersampling_Median Imput...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.390308             NaN      Zero Imputation   
 1    Logistic Regression  0.081386             NaN      Zero Imputation   
 2    Logistic Regression  0.852321             NaN      Zero Imputation   
 3    Logistic Regression  0.148584             NaN      Zero Imputation   
 4    Logistic Regression  0.636137             NaN      Zero Imputation   
 5    Logistic Regression  0.249106             NaN      Zero Imputation   
 6    Logistic Regression  0.272274             NaN      Zero Imputation   
 7    Logistic Regression       NaN             1.0      Zero Imputation   
 8    Logistic Regression       NaN            None      Zero Imputation   
 9    Logistic Regression       NaN           False      Zero Imputation   
 10   Logistic Regression       NaN            True      Zero Imputation   
 11   Logistic Regression       NaN               1      Zero Imputation   
 12   Logistic Regression       NaN            None      Zero Imputation   
 13   Logistic Regression       NaN             100      Zero Imputation   
 14   Logistic Regression       NaN            auto      Zero Imputation   
 15   Logistic Regression       NaN            None      Zero Imputation   
 16   Logistic Regression       NaN              l2      Zero Imputation   
 17   Logistic Regression       NaN            None      Zero Imputation   
 18   Logistic Regression       NaN           lbfgs      Zero Imputation   
 19   Logistic Regression       NaN          0.0001      Zero Imputation   
 20   Logistic Regression       NaN               0      Zero Imputation   
 21   Logistic Regression       NaN           False      Zero Imputation   
 22   Logistic Regression  0.360284             NaN      Zero Imputation   
 23   Logistic Regression  0.054660             NaN      Zero Imputation   
 24   Logistic Regression  0.847561             NaN      Zero Imputation   
 25   Logistic Regression  0.102697             NaN      Zero Imputation   
 26   Logistic Regression  0.648449             NaN      Zero Imputation   
 27   Logistic Regression  0.250274             NaN      Zero Imputation   
 28   Logistic Regression  0.296897             NaN      Zero Imputation   
 29   Logistic Regression       NaN             1.0      Zero Imputation   
 30   Logistic Regression       NaN            None      Zero Imputation   
 31   Logistic Regression       NaN           False      Zero Imputation   
 32   Logistic Regression       NaN            True      Zero Imputation   
 33   Logistic Regression       NaN               1      Zero Imputation   
 34   Logistic Regression       NaN            None      Zero Imputation   
 35   Logistic Regression       NaN             100      Zero Imputation   
 36   Logistic Regression       NaN            auto      Zero Imputation   
 37   Logistic Regression       NaN            None      Zero Imputation   
 38   Logistic Regression       NaN              l2      Zero Imputation   
 39   Logistic Regression       NaN            None      Zero Imputation   
 40   Logistic Regression       NaN           lbfgs      Zero Imputation   
 41   Logistic Regression       NaN          0.0001      Zero Imputation   
 42   Logistic Regression       NaN               0      Zero Imputation   
 43   Logistic Regression       NaN           False      Zero Imputation   
 44   Logistic Regression  0.363972             NaN      Zero Imputation   
 45   Logistic Regression  0.060726             NaN      Zero Imputation   
 46   Logistic Regression  0.823529             NaN      Zero Imputation   
 47   Logistic Regression  0.113111             NaN      Zero Imputation   
 48   Logistic Regression  0.646102             NaN      Zero Imputation   
 49   Logistic Regression  0.209116             NaN      Zero Imputation   
 50   Logistic Regression  0.292204             NaN      Zero Imputation   
 51   Logistic Regression       NaN             1.0      Zero Imputation   
 52   Logistic Regression       NaN            None      Zero Imputation   
 53   Logistic Regression       NaN           False      Zero Imputation   
 54   Logistic Regression       NaN            True      Zero Imputation   
 55   Logistic Regression       NaN               1      Zero Imputation   
 56   Logistic Regression       NaN            None      Zero Imputation   
 57   Logistic Regression       NaN             100      Zero Imputation   
 58   Logistic Regression       NaN            auto      Zero Imputation   
 59   Logistic Regression       NaN            None      Zero Imputation   
 60   Logistic Regression       NaN              l2      Zero Imputation   
 61   Logistic Regression       NaN            None      Zero Imputation   
 62   Logistic Regression       NaN           lbfgs      Zero Imputation   
 63   Logistic Regression       NaN          0.0001      Zero Imputation   
 64   Logistic Regression       NaN               0      Zero Imputation   
 65   Logistic Regression       NaN           False      Zero Imputation   
 66   Logistic Regression  0.345627             NaN      Zero Imputation   
 67   Logistic Regression  0.063359             NaN      Zero Imputation   
 68   Logistic Regression  0.846939             NaN      Zero Imputation   
 69   Logistic Regression  0.117898             NaN      Zero Imputation   
 70   Logistic Regression  0.658314             NaN      Zero Imputation   
 71   Logistic Regression  0.252976             NaN      Zero Imputation   
 72   Logistic Regression  0.316628             NaN      Zero Imputation   
 73   Logistic Regression       NaN             1.0      Zero Imputation   
 74   Logistic Regression       NaN            None      Zero Imputation   
 75   Logistic Regression       NaN           False      Zero Imputation   
 76   Logistic Regression       NaN            True      Zero Imputation   
 77   Logistic Regression       NaN               1      Zero Imputation   
 78   Logistic Regression       NaN            None      Zero Imputation   
 79   Logistic Regression       NaN             100      Zero Imputation   
 80   Logistic Regression       NaN            auto      Zero Imputation   
 81   Logistic Regression       NaN            None      Zero Imputation   
 82   Logistic Regression       NaN              l2      Zero Imputation   
 83   Logistic Regression       NaN            None      Zero Imputation   
 84   Logistic Regression       NaN           lbfgs      Zero Imputation   
 85   Logistic Regression       NaN          0.0001      Zero Imputation   
 86   Logistic Regression       NaN               0      Zero Imputation   
 87   Logistic Regression       NaN           False      Zero Imputation   
 88   Logistic Regression  0.380664             NaN      Zero Imputation   
 89   Logistic Regression  0.068687             NaN      Zero Imputation   
 90   Logistic Regression  0.787037             NaN      Zero Imputation   
 91   Logistic Regression  0.126347             NaN      Zero Imputation   
 92   Logistic Regression  0.643175             NaN      Zero Imputation   
 93   Logistic Regression  0.249581             NaN      Zero Imputation   
 94   Logistic Regression  0.286349             NaN      Zero Imputation   
 95   Logistic Regression       NaN             1.0      Zero Imputation   
 96   Logistic Regression       NaN            None      Zero Imputation   
 97   Logistic Regression       NaN           False      Zero Imputation   
 98   Logistic Regression       NaN            True      Zero Imputation   
 99   Logistic Regression       NaN               1      Zero Imputation   
 100  Logistic Regression       NaN            None      Zero Imputation   
 101  Logistic Regression       NaN             100      Zero Imputation   
 102  Logistic Regression       NaN            auto      Zero Imputation   
 103  Logistic Regression       NaN            None      Zero Imputation   
 104  Logistic Regression       NaN              l2      Zero Imputation   
 105  Logistic Regression       NaN            None      Zero Imputation   
 106  Logistic Regression       NaN           lbfgs      Zero Imputation   
 107  Logistic Regression       NaN          0.0001      Zero Imputation   
 108  Logistic Regression       NaN               0      Zero Imputation   
 109  Logistic Regression       NaN           False      Zero Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 1    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 2    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 3    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 4    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 5    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 6    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 7    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 8    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 9    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 10   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 11   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 12   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 13   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 14   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 15   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 16   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 17   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 18   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 19   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 20   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 21   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 22   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 23   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 24   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 25   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 26   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 27   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 28   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 29   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 30   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 31   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 32   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 33   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 34   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 35   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 36   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 37   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 38   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 39   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 40   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 41   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 42   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 43   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 44   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 45   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 46   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 47   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 48   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 49   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 50   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 51   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 52   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 53   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 54   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 55   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 56   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 57   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 58   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 59   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 60   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 61   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 62   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 63   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 64   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 65   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 66   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 67   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 68   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 69   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 70   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 71   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 72   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 73   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 74   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 75   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 76   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 77   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 78   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 79   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 80   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 81   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 82   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 83   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 84   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 85   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 86   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 87   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 88   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 89   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 90   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 91   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 92   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 93   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 94   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 95   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 96   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 97   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 98   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 99   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 100  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 101  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 102  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 103  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 104  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 105  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 106  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 107  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 108  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 109  Logistic Regression_Hybrid Sampling (SMOTEENN)...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.366869             NaN      Mean Imputation   
 1    Logistic Regression  0.078569             NaN      Mean Imputation   
 2    Logistic Regression  0.852321             NaN      Mean Imputation   
 3    Logistic Regression  0.143875             NaN      Mean Imputation   
 4    Logistic Regression  0.638731             NaN      Mean Imputation   
 5    Logistic Regression  0.265836             NaN      Mean Imputation   
 6    Logistic Regression  0.277463             NaN      Mean Imputation   
 7    Logistic Regression       NaN             1.0      Mean Imputation   
 8    Logistic Regression       NaN            None      Mean Imputation   
 9    Logistic Regression       NaN           False      Mean Imputation   
 10   Logistic Regression       NaN            True      Mean Imputation   
 11   Logistic Regression       NaN               1      Mean Imputation   
 12   Logistic Regression       NaN            None      Mean Imputation   
 13   Logistic Regression       NaN             100      Mean Imputation   
 14   Logistic Regression       NaN            auto      Mean Imputation   
 15   Logistic Regression       NaN            None      Mean Imputation   
 16   Logistic Regression       NaN              l2      Mean Imputation   
 17   Logistic Regression       NaN            None      Mean Imputation   
 18   Logistic Regression       NaN           lbfgs      Mean Imputation   
 19   Logistic Regression       NaN          0.0001      Mean Imputation   
 20   Logistic Regression       NaN               0      Mean Imputation   
 21   Logistic Regression       NaN           False      Mean Imputation   
 22   Logistic Regression  0.366605             NaN      Mean Imputation   
 23   Logistic Regression  0.054119             NaN      Mean Imputation   
 24   Logistic Regression  0.829268             NaN      Mean Imputation   
 25   Logistic Regression  0.101606             NaN      Mean Imputation   
 26   Logistic Regression  0.621086             NaN      Mean Imputation   
 27   Logistic Regression  0.253003             NaN      Mean Imputation   
 28   Logistic Regression  0.242172             NaN      Mean Imputation   
 29   Logistic Regression       NaN             1.0      Mean Imputation   
 30   Logistic Regression       NaN            None      Mean Imputation   
 31   Logistic Regression       NaN           False      Mean Imputation   
 32   Logistic Regression       NaN            True      Mean Imputation   
 33   Logistic Regression       NaN               1      Mean Imputation   
 34   Logistic Regression       NaN            None      Mean Imputation   
 35   Logistic Regression       NaN             100      Mean Imputation   
 36   Logistic Regression       NaN            auto      Mean Imputation   
 37   Logistic Regression       NaN            None      Mean Imputation   
 38   Logistic Regression       NaN              l2      Mean Imputation   
 39   Logistic Regression       NaN            None      Mean Imputation   
 40   Logistic Regression       NaN           lbfgs      Mean Imputation   
 41   Logistic Regression       NaN          0.0001      Mean Imputation   
 42   Logistic Regression       NaN               0      Mean Imputation   
 43   Logistic Regression       NaN           False      Mean Imputation   
 44   Logistic Regression  0.357651             NaN      Mean Imputation   
 45   Logistic Regression  0.061526             NaN      Mean Imputation   
 46   Logistic Regression  0.844920             NaN      Mean Imputation   
 47   Logistic Regression  0.114701             NaN      Mean Imputation   
 48   Logistic Regression  0.629918             NaN      Mean Imputation   
 49   Logistic Regression  0.263764             NaN      Mean Imputation   
 50   Logistic Regression  0.259837             NaN      Mean Imputation   
 51   Logistic Regression       NaN             1.0      Mean Imputation   
 52   Logistic Regression       NaN            None      Mean Imputation   
 53   Logistic Regression       NaN           False      Mean Imputation   
 54   Logistic Regression       NaN            True      Mean Imputation   
 55   Logistic Regression       NaN               1      Mean Imputation   
 56   Logistic Regression       NaN            None      Mean Imputation   
 57   Logistic Regression       NaN             100      Mean Imputation   
 58   Logistic Regression       NaN            auto      Mean Imputation   
 59   Logistic Regression       NaN            None      Mean Imputation   
 60   Logistic Regression       NaN              l2      Mean Imputation   
 61   Logistic Regression       NaN            None      Mean Imputation   
 62   Logistic Regression       NaN           lbfgs      Mean Imputation   
 63   Logistic Regression       NaN          0.0001      Mean Imputation   
 64   Logistic Regression       NaN               0      Mean Imputation   
 65   Logistic Regression       NaN           False      Mean Imputation   
 66   Logistic Regression  0.362750             NaN      Mean Imputation   
 67   Logistic Regression  0.063604             NaN      Mean Imputation   
 68   Logistic Regression  0.826531             NaN      Mean Imputation   
 69   Logistic Regression  0.118119             NaN      Mean Imputation   
 70   Logistic Regression  0.605573             NaN      Mean Imputation   
 71   Logistic Regression  0.217251             NaN      Mean Imputation   
 72   Logistic Regression  0.211147             NaN      Mean Imputation   
 73   Logistic Regression       NaN             1.0      Mean Imputation   
 74   Logistic Regression       NaN            None      Mean Imputation   
 75   Logistic Regression       NaN           False      Mean Imputation   
 76   Logistic Regression       NaN            True      Mean Imputation   
 77   Logistic Regression       NaN               1      Mean Imputation   
 78   Logistic Regression       NaN            None      Mean Imputation   
 79   Logistic Regression       NaN             100      Mean Imputation   
 80   Logistic Regression       NaN            auto      Mean Imputation   
 81   Logistic Regression       NaN            None      Mean Imputation   
 82   Logistic Regression       NaN              l2      Mean Imputation   
 83   Logistic Regression       NaN            None      Mean Imputation   
 84   Logistic Regression       NaN           lbfgs      Mean Imputation   
 85   Logistic Regression       NaN          0.0001      Mean Imputation   
 86   Logistic Regression       NaN               0      Mean Imputation   
 87   Logistic Regression       NaN           False      Mean Imputation   
 88   Logistic Regression  0.388830             NaN      Mean Imputation   
 89   Logistic Regression  0.070962             NaN      Mean Imputation   
 90   Logistic Regression  0.805556             NaN      Mean Imputation   
 91   Logistic Regression  0.130435             NaN      Mean Imputation   
 92   Logistic Regression  0.612938             NaN      Mean Imputation   
 93   Logistic Regression  0.218606             NaN      Mean Imputation   
 94   Logistic Regression  0.225875             NaN      Mean Imputation   
 95   Logistic Regression       NaN             1.0      Mean Imputation   
 96   Logistic Regression       NaN            None      Mean Imputation   
 97   Logistic Regression       NaN           False      Mean Imputation   
 98   Logistic Regression       NaN            True      Mean Imputation   
 99   Logistic Regression       NaN               1      Mean Imputation   
 100  Logistic Regression       NaN            None      Mean Imputation   
 101  Logistic Regression       NaN             100      Mean Imputation   
 102  Logistic Regression       NaN            auto      Mean Imputation   
 103  Logistic Regression       NaN            None      Mean Imputation   
 104  Logistic Regression       NaN              l2      Mean Imputation   
 105  Logistic Regression       NaN            None      Mean Imputation   
 106  Logistic Regression       NaN           lbfgs      Mean Imputation   
 107  Logistic Regression       NaN          0.0001      Mean Imputation   
 108  Logistic Regression       NaN               0      Mean Imputation   
 109  Logistic Regression       NaN           False      Mean Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 1    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 2    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 3    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 4    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 5    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 6    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 7    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 8    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 9    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 10   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 11   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 12   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 13   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 14   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 15   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 16   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 17   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 18   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 19   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 20   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 21   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 22   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 23   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 24   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 25   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 26   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 27   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 28   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 29   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 30   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 31   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 32   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 33   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 34   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 35   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 36   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 37   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 38   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 39   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 40   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 41   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 42   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 43   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 44   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 45   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 46   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 47   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 48   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 49   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 50   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 51   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 52   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 53   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 54   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 55   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 56   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 57   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 58   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 59   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 60   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 61   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 62   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 63   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 64   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 65   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 66   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 67   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 68   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 69   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 70   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 71   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 72   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 73   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 74   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 75   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 76   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 77   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 78   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 79   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 80   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 81   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 82   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 83   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 84   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 85   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 86   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 87   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 88   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 89   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 90   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 91   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 92   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 93   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 94   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 95   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 96   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 97   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 98   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 99   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 100  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 101  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 102  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 103  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 104  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 105  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 106  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 107  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 108  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 109  Logistic Regression_Hybrid Sampling (SMOTEENN)...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.411114             NaN    Median Imputation   
 1    Logistic Regression  0.082323             NaN    Median Imputation   
 2    Logistic Regression  0.831224             NaN    Median Imputation   
 3    Logistic Regression  0.149810             NaN    Median Imputation   
 4    Logistic Regression  0.633390             NaN    Median Imputation   
 5    Logistic Regression  0.238683             NaN    Median Imputation   
 6    Logistic Regression  0.266779             NaN    Median Imputation   
 7    Logistic Regression       NaN             1.0    Median Imputation   
 8    Logistic Regression       NaN            None    Median Imputation   
 9    Logistic Regression       NaN           False    Median Imputation   
 10   Logistic Regression       NaN            True    Median Imputation   
 11   Logistic Regression       NaN               1    Median Imputation   
 12   Logistic Regression       NaN            None    Median Imputation   
 13   Logistic Regression       NaN             100    Median Imputation   
 14   Logistic Regression       NaN            auto    Median Imputation   
 15   Logistic Regression       NaN            None    Median Imputation   
 16   Logistic Regression       NaN              l2    Median Imputation   
 17   Logistic Regression       NaN            None    Median Imputation   
 18   Logistic Regression       NaN           lbfgs    Median Imputation   
 19   Logistic Regression       NaN          0.0001    Median Imputation   
 20   Logistic Regression       NaN               0    Median Imputation   
 21   Logistic Regression       NaN           False    Median Imputation   
 22   Logistic Regression  0.384251             NaN    Median Imputation   
 23   Logistic Regression  0.057050             NaN    Median Imputation   
 24   Logistic Regression  0.853659             NaN    Median Imputation   
 25   Logistic Regression  0.106952             NaN    Median Imputation   
 26   Logistic Regression  0.642791             NaN    Median Imputation   
 27   Logistic Regression  0.247553             NaN    Median Imputation   
 28   Logistic Regression  0.285582             NaN    Median Imputation   
 29   Logistic Regression       NaN             1.0    Median Imputation   
 30   Logistic Regression       NaN            None    Median Imputation   
 31   Logistic Regression       NaN           False    Median Imputation   
 32   Logistic Regression       NaN            True    Median Imputation   
 33   Logistic Regression       NaN               1    Median Imputation   
 34   Logistic Regression       NaN            None    Median Imputation   
 35   Logistic Regression       NaN             100    Median Imputation   
 36   Logistic Regression       NaN            auto    Median Imputation   
 37   Logistic Regression       NaN            None    Median Imputation   
 38   Logistic Regression       NaN              l2    Median Imputation   
 39   Logistic Regression       NaN            None    Median Imputation   
 40   Logistic Regression       NaN           lbfgs    Median Imputation   
 41   Logistic Regression       NaN          0.0001    Median Imputation   
 42   Logistic Regression       NaN               0    Median Imputation   
 43   Logistic Regression       NaN           False    Median Imputation   
 44   Logistic Regression  0.373189             NaN    Median Imputation   
 45   Logistic Regression  0.061926             NaN    Median Imputation   
 46   Logistic Regression  0.828877             NaN    Median Imputation   
 47   Logistic Regression  0.115242             NaN    Median Imputation   
 48   Logistic Regression  0.648276             NaN    Median Imputation   
 49   Logistic Regression  0.271847             NaN    Median Imputation   
 50   Logistic Regression  0.296553             NaN    Median Imputation   
 51   Logistic Regression       NaN             1.0    Median Imputation   
 52   Logistic Regression       NaN            None    Median Imputation   
 53   Logistic Regression       NaN           False    Median Imputation   
 54   Logistic Regression       NaN            True    Median Imputation   
 55   Logistic Regression       NaN               1    Median Imputation   
 56   Logistic Regression       NaN            None    Median Imputation   
 57   Logistic Regression       NaN             100    Median Imputation   
 58   Logistic Regression       NaN            auto    Median Imputation   
 59   Logistic Regression       NaN            None    Median Imputation   
 60   Logistic Regression       NaN              l2    Median Imputation   
 61   Logistic Regression       NaN            None    Median Imputation   
 62   Logistic Regression       NaN           lbfgs    Median Imputation   
 63   Logistic Regression       NaN          0.0001    Median Imputation   
 64   Logistic Regression       NaN               0    Median Imputation   
 65   Logistic Regression       NaN           False    Median Imputation   
 66   Logistic Regression  0.372234             NaN    Median Imputation   
 67   Logistic Regression  0.065209             NaN    Median Imputation   
 68   Logistic Regression  0.836735             NaN    Median Imputation   
 69   Logistic Regression  0.120989             NaN    Median Imputation   
 70   Logistic Regression  0.651721             NaN    Median Imputation   
 71   Logistic Regression  0.253787             NaN    Median Imputation   
 72   Logistic Regression  0.303442             NaN    Median Imputation   
 73   Logistic Regression       NaN             1.0    Median Imputation   
 74   Logistic Regression       NaN            None    Median Imputation   
 75   Logistic Regression       NaN           False    Median Imputation   
 76   Logistic Regression       NaN            True    Median Imputation   
 77   Logistic Regression       NaN               1    Median Imputation   
 78   Logistic Regression       NaN            None    Median Imputation   
 79   Logistic Regression       NaN             100    Median Imputation   
 80   Logistic Regression       NaN            auto    Median Imputation   
 81   Logistic Regression       NaN            None    Median Imputation   
 82   Logistic Regression       NaN              l2    Median Imputation   
 83   Logistic Regression       NaN            None    Median Imputation   
 84   Logistic Regression       NaN           lbfgs    Median Imputation   
 85   Logistic Regression       NaN          0.0001    Median Imputation   
 86   Logistic Regression       NaN               0    Median Imputation   
 87   Logistic Regression       NaN           False    Median Imputation   
 88   Logistic Regression  0.397524             NaN    Median Imputation   
 89   Logistic Regression  0.072637             NaN    Median Imputation   
 90   Logistic Regression  0.814815             NaN    Median Imputation   
 91   Logistic Regression  0.133384             NaN    Median Imputation   
 92   Logistic Regression  0.632641             NaN    Median Imputation   
 93   Logistic Regression  0.254252             NaN    Median Imputation   
 94   Logistic Regression  0.265283             NaN    Median Imputation   
 95   Logistic Regression       NaN             1.0    Median Imputation   
 96   Logistic Regression       NaN            None    Median Imputation   
 97   Logistic Regression       NaN           False    Median Imputation   
 98   Logistic Regression       NaN            True    Median Imputation   
 99   Logistic Regression       NaN               1    Median Imputation   
 100  Logistic Regression       NaN            None    Median Imputation   
 101  Logistic Regression       NaN             100    Median Imputation   
 102  Logistic Regression       NaN            auto    Median Imputation   
 103  Logistic Regression       NaN            None    Median Imputation   
 104  Logistic Regression       NaN              l2    Median Imputation   
 105  Logistic Regression       NaN            None    Median Imputation   
 106  Logistic Regression       NaN           lbfgs    Median Imputation   
 107  Logistic Regression       NaN          0.0001    Median Imputation   
 108  Logistic Regression       NaN               0    Median Imputation   
 109  Logistic Regression       NaN           False    Median Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 1    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 2    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 3    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 4    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 5    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 6    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 7    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 8    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 9    Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 10   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 11   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 12   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 13   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 14   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 15   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 16   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 17   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 18   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 19   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 20   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 21   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 22   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 23   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 24   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 25   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 26   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 27   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 28   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 29   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 30   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 31   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 32   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 33   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 34   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 35   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 36   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 37   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 38   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 39   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 40   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 41   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 42   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 43   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 44   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 45   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 46   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 47   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 48   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 49   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 50   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 51   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 52   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 53   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 54   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 55   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 56   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 57   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 58   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 59   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 60   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 61   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 62   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 63   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 64   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 65   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 66   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 67   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 68   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 69   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 70   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 71   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 72   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 73   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 74   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 75   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 76   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 77   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 78   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 79   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 80   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 81   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 82   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 83   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 84   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 85   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 86   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 87   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 88   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 89   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 90   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 91   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 92   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 93   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 94   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 95   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 96   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 97   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 98   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 99   Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 100  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 101  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 102  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 103  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 104  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 105  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 106  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 107  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 108  Logistic Regression_Hybrid Sampling (SMOTEENN)...  
 109  Logistic Regression_Hybrid Sampling (SMOTEENN)...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.472742             NaN      Zero Imputation   
 1    Logistic Regression  0.083923             NaN      Zero Imputation   
 2    Logistic Regression  0.751055             NaN      Zero Imputation   
 3    Logistic Regression  0.150975             NaN      Zero Imputation   
 4    Logistic Regression  0.626554             NaN      Zero Imputation   
 5    Logistic Regression  0.247362             NaN      Zero Imputation   
 6    Logistic Regression  0.253109             NaN      Zero Imputation   
 7    Logistic Regression       NaN             1.0      Zero Imputation   
 8    Logistic Regression       NaN            None      Zero Imputation   
 9    Logistic Regression       NaN           False      Zero Imputation   
 10   Logistic Regression       NaN            True      Zero Imputation   
 11   Logistic Regression       NaN               1      Zero Imputation   
 12   Logistic Regression       NaN            None      Zero Imputation   
 13   Logistic Regression       NaN             100      Zero Imputation   
 14   Logistic Regression       NaN            auto      Zero Imputation   
 15   Logistic Regression       NaN            None      Zero Imputation   
 16   Logistic Regression       NaN              l2      Zero Imputation   
 17   Logistic Regression       NaN            None      Zero Imputation   
 18   Logistic Regression       NaN           lbfgs      Zero Imputation   
 19   Logistic Regression       NaN          0.0001      Zero Imputation   
 20   Logistic Regression       NaN               0      Zero Imputation   
 21   Logistic Regression       NaN           False      Zero Imputation   
 22   Logistic Regression  0.470635             NaN      Zero Imputation   
 23   Logistic Regression  0.057950             NaN      Zero Imputation   
 24   Logistic Regression  0.737805             NaN      Zero Imputation   
 25   Logistic Regression  0.107460             NaN      Zero Imputation   
 26   Logistic Regression  0.643828             NaN      Zero Imputation   
 27   Logistic Regression  0.232872             NaN      Zero Imputation   
 28   Logistic Regression  0.287656             NaN      Zero Imputation   
 29   Logistic Regression       NaN             1.0      Zero Imputation   
 30   Logistic Regression       NaN            None      Zero Imputation   
 31   Logistic Regression       NaN           False      Zero Imputation   
 32   Logistic Regression       NaN            True      Zero Imputation   
 33   Logistic Regression       NaN               1      Zero Imputation   
 34   Logistic Regression       NaN            None      Zero Imputation   
 35   Logistic Regression       NaN             100      Zero Imputation   
 36   Logistic Regression       NaN            auto      Zero Imputation   
 37   Logistic Regression       NaN            None      Zero Imputation   
 38   Logistic Regression       NaN              l2      Zero Imputation   
 39   Logistic Regression       NaN            None      Zero Imputation   
 40   Logistic Regression       NaN           lbfgs      Zero Imputation   
 41   Logistic Regression       NaN          0.0001      Zero Imputation   
 42   Logistic Regression       NaN               0      Zero Imputation   
 43   Logistic Regression       NaN           False      Zero Imputation   
 44   Logistic Regression  0.468791             NaN      Zero Imputation   
 45   Logistic Regression  0.061361             NaN      Zero Imputation   
 46   Logistic Regression  0.684492             NaN      Zero Imputation   
 47   Logistic Regression  0.112626             NaN      Zero Imputation   
 48   Logistic Regression  0.640194             NaN      Zero Imputation   
 49   Logistic Regression  0.209657             NaN      Zero Imputation   
 50   Logistic Regression  0.280389             NaN      Zero Imputation   
 51   Logistic Regression       NaN             1.0      Zero Imputation   
 52   Logistic Regression       NaN            None      Zero Imputation   
 53   Logistic Regression       NaN           False      Zero Imputation   
 54   Logistic Regression       NaN            True      Zero Imputation   
 55   Logistic Regression       NaN               1      Zero Imputation   
 56   Logistic Regression       NaN            None      Zero Imputation   
 57   Logistic Regression       NaN             100      Zero Imputation   
 58   Logistic Regression       NaN            auto      Zero Imputation   
 59   Logistic Regression       NaN            None      Zero Imputation   
 60   Logistic Regression       NaN              l2      Zero Imputation   
 61   Logistic Regression       NaN            None      Zero Imputation   
 62   Logistic Regression       NaN           lbfgs      Zero Imputation   
 63   Logistic Regression       NaN          0.0001      Zero Imputation   
 64   Logistic Regression       NaN               0      Zero Imputation   
 65   Logistic Regression       NaN           False      Zero Imputation   
 66   Logistic Regression  0.450738             NaN      Zero Imputation   
 67   Logistic Regression  0.067735             NaN      Zero Imputation   
 68   Logistic Regression  0.755102             NaN      Zero Imputation   
 69   Logistic Regression  0.124318             NaN      Zero Imputation   
 70   Logistic Regression  0.660009             NaN      Zero Imputation   
 71   Logistic Regression  0.246412             NaN      Zero Imputation   
 72   Logistic Regression  0.320018             NaN      Zero Imputation   
 73   Logistic Regression       NaN             1.0      Zero Imputation   
 74   Logistic Regression       NaN            None      Zero Imputation   
 75   Logistic Regression       NaN           False      Zero Imputation   
 76   Logistic Regression       NaN            True      Zero Imputation   
 77   Logistic Regression       NaN               1      Zero Imputation   
 78   Logistic Regression       NaN            None      Zero Imputation   
 79   Logistic Regression       NaN             100      Zero Imputation   
 80   Logistic Regression       NaN            auto      Zero Imputation   
 81   Logistic Regression       NaN            None      Zero Imputation   
 82   Logistic Regression       NaN              l2      Zero Imputation   
 83   Logistic Regression       NaN            None      Zero Imputation   
 84   Logistic Regression       NaN           lbfgs      Zero Imputation   
 85   Logistic Regression       NaN          0.0001      Zero Imputation   
 86   Logistic Regression       NaN               0      Zero Imputation   
 87   Logistic Regression       NaN           False      Zero Imputation   
 88   Logistic Regression  0.494731             NaN      Zero Imputation   
 89   Logistic Regression  0.075773             NaN      Zero Imputation   
 90   Logistic Regression  0.703704             NaN      Zero Imputation   
 91   Logistic Regression  0.136814             NaN      Zero Imputation   
 92   Logistic Regression  0.647318             NaN      Zero Imputation   
 93   Logistic Regression  0.237746             NaN      Zero Imputation   
 94   Logistic Regression  0.294636             NaN      Zero Imputation   
 95   Logistic Regression       NaN             1.0      Zero Imputation   
 96   Logistic Regression       NaN            None      Zero Imputation   
 97   Logistic Regression       NaN           False      Zero Imputation   
 98   Logistic Regression       NaN            True      Zero Imputation   
 99   Logistic Regression       NaN               1      Zero Imputation   
 100  Logistic Regression       NaN            None      Zero Imputation   
 101  Logistic Regression       NaN             100      Zero Imputation   
 102  Logistic Regression       NaN            auto      Zero Imputation   
 103  Logistic Regression       NaN            None      Zero Imputation   
 104  Logistic Regression       NaN              l2      Zero Imputation   
 105  Logistic Regression       NaN            None      Zero Imputation   
 106  Logistic Regression       NaN           lbfgs      Zero Imputation   
 107  Logistic Regression       NaN          0.0001      Zero Imputation   
 108  Logistic Regression       NaN               0      Zero Imputation   
 109  Logistic Regression       NaN           False      Zero Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTETome...  
 1    Logistic Regression_Hybrid Sampling (SMOTETome...  
 2    Logistic Regression_Hybrid Sampling (SMOTETome...  
 3    Logistic Regression_Hybrid Sampling (SMOTETome...  
 4    Logistic Regression_Hybrid Sampling (SMOTETome...  
 5    Logistic Regression_Hybrid Sampling (SMOTETome...  
 6    Logistic Regression_Hybrid Sampling (SMOTETome...  
 7    Logistic Regression_Hybrid Sampling (SMOTETome...  
 8    Logistic Regression_Hybrid Sampling (SMOTETome...  
 9    Logistic Regression_Hybrid Sampling (SMOTETome...  
 10   Logistic Regression_Hybrid Sampling (SMOTETome...  
 11   Logistic Regression_Hybrid Sampling (SMOTETome...  
 12   Logistic Regression_Hybrid Sampling (SMOTETome...  
 13   Logistic Regression_Hybrid Sampling (SMOTETome...  
 14   Logistic Regression_Hybrid Sampling (SMOTETome...  
 15   Logistic Regression_Hybrid Sampling (SMOTETome...  
 16   Logistic Regression_Hybrid Sampling (SMOTETome...  
 17   Logistic Regression_Hybrid Sampling (SMOTETome...  
 18   Logistic Regression_Hybrid Sampling (SMOTETome...  
 19   Logistic Regression_Hybrid Sampling (SMOTETome...  
 20   Logistic Regression_Hybrid Sampling (SMOTETome...  
 21   Logistic Regression_Hybrid Sampling (SMOTETome...  
 22   Logistic Regression_Hybrid Sampling (SMOTETome...  
 23   Logistic Regression_Hybrid Sampling (SMOTETome...  
 24   Logistic Regression_Hybrid Sampling (SMOTETome...  
 25   Logistic Regression_Hybrid Sampling (SMOTETome...  
 26   Logistic Regression_Hybrid Sampling (SMOTETome...  
 27   Logistic Regression_Hybrid Sampling (SMOTETome...  
 28   Logistic Regression_Hybrid Sampling (SMOTETome...  
 29   Logistic Regression_Hybrid Sampling (SMOTETome...  
 30   Logistic Regression_Hybrid Sampling (SMOTETome...  
 31   Logistic Regression_Hybrid Sampling (SMOTETome...  
 32   Logistic Regression_Hybrid Sampling (SMOTETome...  
 33   Logistic Regression_Hybrid Sampling (SMOTETome...  
 34   Logistic Regression_Hybrid Sampling (SMOTETome...  
 35   Logistic Regression_Hybrid Sampling (SMOTETome...  
 36   Logistic Regression_Hybrid Sampling (SMOTETome...  
 37   Logistic Regression_Hybrid Sampling (SMOTETome...  
 38   Logistic Regression_Hybrid Sampling (SMOTETome...  
 39   Logistic Regression_Hybrid Sampling (SMOTETome...  
 40   Logistic Regression_Hybrid Sampling (SMOTETome...  
 41   Logistic Regression_Hybrid Sampling (SMOTETome...  
 42   Logistic Regression_Hybrid Sampling (SMOTETome...  
 43   Logistic Regression_Hybrid Sampling (SMOTETome...  
 44   Logistic Regression_Hybrid Sampling (SMOTETome...  
 45   Logistic Regression_Hybrid Sampling (SMOTETome...  
 46   Logistic Regression_Hybrid Sampling (SMOTETome...  
 47   Logistic Regression_Hybrid Sampling (SMOTETome...  
 48   Logistic Regression_Hybrid Sampling (SMOTETome...  
 49   Logistic Regression_Hybrid Sampling (SMOTETome...  
 50   Logistic Regression_Hybrid Sampling (SMOTETome...  
 51   Logistic Regression_Hybrid Sampling (SMOTETome...  
 52   Logistic Regression_Hybrid Sampling (SMOTETome...  
 53   Logistic Regression_Hybrid Sampling (SMOTETome...  
 54   Logistic Regression_Hybrid Sampling (SMOTETome...  
 55   Logistic Regression_Hybrid Sampling (SMOTETome...  
 56   Logistic Regression_Hybrid Sampling (SMOTETome...  
 57   Logistic Regression_Hybrid Sampling (SMOTETome...  
 58   Logistic Regression_Hybrid Sampling (SMOTETome...  
 59   Logistic Regression_Hybrid Sampling (SMOTETome...  
 60   Logistic Regression_Hybrid Sampling (SMOTETome...  
 61   Logistic Regression_Hybrid Sampling (SMOTETome...  
 62   Logistic Regression_Hybrid Sampling (SMOTETome...  
 63   Logistic Regression_Hybrid Sampling (SMOTETome...  
 64   Logistic Regression_Hybrid Sampling (SMOTETome...  
 65   Logistic Regression_Hybrid Sampling (SMOTETome...  
 66   Logistic Regression_Hybrid Sampling (SMOTETome...  
 67   Logistic Regression_Hybrid Sampling (SMOTETome...  
 68   Logistic Regression_Hybrid Sampling (SMOTETome...  
 69   Logistic Regression_Hybrid Sampling (SMOTETome...  
 70   Logistic Regression_Hybrid Sampling (SMOTETome...  
 71   Logistic Regression_Hybrid Sampling (SMOTETome...  
 72   Logistic Regression_Hybrid Sampling (SMOTETome...  
 73   Logistic Regression_Hybrid Sampling (SMOTETome...  
 74   Logistic Regression_Hybrid Sampling (SMOTETome...  
 75   Logistic Regression_Hybrid Sampling (SMOTETome...  
 76   Logistic Regression_Hybrid Sampling (SMOTETome...  
 77   Logistic Regression_Hybrid Sampling (SMOTETome...  
 78   Logistic Regression_Hybrid Sampling (SMOTETome...  
 79   Logistic Regression_Hybrid Sampling (SMOTETome...  
 80   Logistic Regression_Hybrid Sampling (SMOTETome...  
 81   Logistic Regression_Hybrid Sampling (SMOTETome...  
 82   Logistic Regression_Hybrid Sampling (SMOTETome...  
 83   Logistic Regression_Hybrid Sampling (SMOTETome...  
 84   Logistic Regression_Hybrid Sampling (SMOTETome...  
 85   Logistic Regression_Hybrid Sampling (SMOTETome...  
 86   Logistic Regression_Hybrid Sampling (SMOTETome...  
 87   Logistic Regression_Hybrid Sampling (SMOTETome...  
 88   Logistic Regression_Hybrid Sampling (SMOTETome...  
 89   Logistic Regression_Hybrid Sampling (SMOTETome...  
 90   Logistic Regression_Hybrid Sampling (SMOTETome...  
 91   Logistic Regression_Hybrid Sampling (SMOTETome...  
 92   Logistic Regression_Hybrid Sampling (SMOTETome...  
 93   Logistic Regression_Hybrid Sampling (SMOTETome...  
 94   Logistic Regression_Hybrid Sampling (SMOTETome...  
 95   Logistic Regression_Hybrid Sampling (SMOTETome...  
 96   Logistic Regression_Hybrid Sampling (SMOTETome...  
 97   Logistic Regression_Hybrid Sampling (SMOTETome...  
 98   Logistic Regression_Hybrid Sampling (SMOTETome...  
 99   Logistic Regression_Hybrid Sampling (SMOTETome...  
 100  Logistic Regression_Hybrid Sampling (SMOTETome...  
 101  Logistic Regression_Hybrid Sampling (SMOTETome...  
 102  Logistic Regression_Hybrid Sampling (SMOTETome...  
 103  Logistic Regression_Hybrid Sampling (SMOTETome...  
 104  Logistic Regression_Hybrid Sampling (SMOTETome...  
 105  Logistic Regression_Hybrid Sampling (SMOTETome...  
 106  Logistic Regression_Hybrid Sampling (SMOTETome...  
 107  Logistic Regression_Hybrid Sampling (SMOTETome...  
 108  Logistic Regression_Hybrid Sampling (SMOTETome...  
 109  Logistic Regression_Hybrid Sampling (SMOTETome...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.449302             NaN      Mean Imputation   
 1    Logistic Regression  0.083183             NaN      Mean Imputation   
 2    Logistic Regression  0.780591             NaN      Mean Imputation   
 3    Logistic Regression  0.150345             NaN      Mean Imputation   
 4    Logistic Regression  0.623066             NaN      Mean Imputation   
 5    Logistic Regression  0.249615             NaN      Mean Imputation   
 6    Logistic Regression  0.246133             NaN      Mean Imputation   
 7    Logistic Regression       NaN             1.0      Mean Imputation   
 8    Logistic Regression       NaN            None      Mean Imputation   
 9    Logistic Regression       NaN           False      Mean Imputation   
 10   Logistic Regression       NaN            True      Mean Imputation   
 11   Logistic Regression       NaN               1      Mean Imputation   
 12   Logistic Regression       NaN            None      Mean Imputation   
 13   Logistic Regression       NaN             100      Mean Imputation   
 14   Logistic Regression       NaN            auto      Mean Imputation   
 15   Logistic Regression       NaN            None      Mean Imputation   
 16   Logistic Regression       NaN              l2      Mean Imputation   
 17   Logistic Regression       NaN            None      Mean Imputation   
 18   Logistic Regression       NaN           lbfgs      Mean Imputation   
 19   Logistic Regression       NaN          0.0001      Mean Imputation   
 20   Logistic Regression       NaN               0      Mean Imputation   
 21   Logistic Regression       NaN           False      Mean Imputation   
 22   Logistic Regression  0.476165             NaN      Mean Imputation   
 23   Logistic Regression  0.056824             NaN      Mean Imputation   
 24   Logistic Regression  0.713415             NaN      Mean Imputation   
 25   Logistic Regression  0.105263             NaN      Mean Imputation   
 26   Logistic Regression  0.621783             NaN      Mean Imputation   
 27   Logistic Regression  0.227297             NaN      Mean Imputation   
 28   Logistic Regression  0.243565             NaN      Mean Imputation   
 29   Logistic Regression       NaN             1.0      Mean Imputation   
 30   Logistic Regression       NaN            None      Mean Imputation   
 31   Logistic Regression       NaN           False      Mean Imputation   
 32   Logistic Regression       NaN            True      Mean Imputation   
 33   Logistic Regression       NaN               1      Mean Imputation   
 34   Logistic Regression       NaN            None      Mean Imputation   
 35   Logistic Regression       NaN             100      Mean Imputation   
 36   Logistic Regression       NaN            auto      Mean Imputation   
 37   Logistic Regression       NaN            None      Mean Imputation   
 38   Logistic Regression       NaN              l2      Mean Imputation   
 39   Logistic Regression       NaN            None      Mean Imputation   
 40   Logistic Regression       NaN           lbfgs      Mean Imputation   
 41   Logistic Regression       NaN          0.0001      Mean Imputation   
 42   Logistic Regression       NaN               0      Mean Imputation   
 43   Logistic Regression       NaN           False      Mean Imputation   
 44   Logistic Regression  0.466948             NaN      Mean Imputation   
 45   Logistic Regression  0.062827             NaN      Mean Imputation   
 46   Logistic Regression  0.705882             NaN      Mean Imputation   
 47   Logistic Regression  0.115385             NaN      Mean Imputation   
 48   Logistic Regression  0.628059             NaN      Mean Imputation   
 49   Logistic Regression  0.218875             NaN      Mean Imputation   
 50   Logistic Regression  0.256119             NaN      Mean Imputation   
 51   Logistic Regression       NaN             1.0      Mean Imputation   
 52   Logistic Regression       NaN            None      Mean Imputation   
 53   Logistic Regression       NaN           False      Mean Imputation   
 54   Logistic Regression       NaN            True      Mean Imputation   
 55   Logistic Regression       NaN               1      Mean Imputation   
 56   Logistic Regression       NaN            None      Mean Imputation   
 57   Logistic Regression       NaN             100      Mean Imputation   
 58   Logistic Regression       NaN            auto      Mean Imputation   
 59   Logistic Regression       NaN            None      Mean Imputation   
 60   Logistic Regression       NaN              l2      Mean Imputation   
 61   Logistic Regression       NaN            None      Mean Imputation   
 62   Logistic Regression       NaN           lbfgs      Mean Imputation   
 63   Logistic Regression       NaN          0.0001      Mean Imputation   
 64   Logistic Regression       NaN               0      Mean Imputation   
 65   Logistic Regression       NaN           False      Mean Imputation   
 66   Logistic Regression  0.512118             NaN      Mean Imputation   
 67   Logistic Regression  0.071872             NaN      Mean Imputation   
 68   Logistic Regression  0.709184             NaN      Mean Imputation   
 69   Logistic Regression  0.130516             NaN      Mean Imputation   
 70   Logistic Regression  0.634318             NaN      Mean Imputation   
 71   Logistic Regression  0.226638             NaN      Mean Imputation   
 72   Logistic Regression  0.268635             NaN      Mean Imputation   
 73   Logistic Regression       NaN             1.0      Mean Imputation   
 74   Logistic Regression       NaN            None      Mean Imputation   
 75   Logistic Regression       NaN           False      Mean Imputation   
 76   Logistic Regression       NaN            True      Mean Imputation   
 77   Logistic Regression       NaN               1      Mean Imputation   
 78   Logistic Regression       NaN            None      Mean Imputation   
 79   Logistic Regression       NaN             100      Mean Imputation   
 80   Logistic Regression       NaN            auto      Mean Imputation   
 81   Logistic Regression       NaN            None      Mean Imputation   
 82   Logistic Regression       NaN              l2      Mean Imputation   
 83   Logistic Regression       NaN            None      Mean Imputation   
 84   Logistic Regression       NaN           lbfgs      Mean Imputation   
 85   Logistic Regression       NaN          0.0001      Mean Imputation   
 86   Logistic Regression       NaN               0      Mean Imputation   
 87   Logistic Regression       NaN           False      Mean Imputation   
 88   Logistic Regression  0.458114             NaN      Mean Imputation   
 89   Logistic Regression  0.075219             NaN      Mean Imputation   
 90   Logistic Regression  0.754630             NaN      Mean Imputation   
 91   Logistic Regression  0.136802             NaN      Mean Imputation   
 92   Logistic Regression  0.632065             NaN      Mean Imputation   
 93   Logistic Regression  0.232583             NaN      Mean Imputation   
 94   Logistic Regression  0.264131             NaN      Mean Imputation   
 95   Logistic Regression       NaN             1.0      Mean Imputation   
 96   Logistic Regression       NaN            None      Mean Imputation   
 97   Logistic Regression       NaN           False      Mean Imputation   
 98   Logistic Regression       NaN            True      Mean Imputation   
 99   Logistic Regression       NaN               1      Mean Imputation   
 100  Logistic Regression       NaN            None      Mean Imputation   
 101  Logistic Regression       NaN             100      Mean Imputation   
 102  Logistic Regression       NaN            auto      Mean Imputation   
 103  Logistic Regression       NaN            None      Mean Imputation   
 104  Logistic Regression       NaN              l2      Mean Imputation   
 105  Logistic Regression       NaN            None      Mean Imputation   
 106  Logistic Regression       NaN           lbfgs      Mean Imputation   
 107  Logistic Regression       NaN          0.0001      Mean Imputation   
 108  Logistic Regression       NaN               0      Mean Imputation   
 109  Logistic Regression       NaN           False      Mean Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTETome...  
 1    Logistic Regression_Hybrid Sampling (SMOTETome...  
 2    Logistic Regression_Hybrid Sampling (SMOTETome...  
 3    Logistic Regression_Hybrid Sampling (SMOTETome...  
 4    Logistic Regression_Hybrid Sampling (SMOTETome...  
 5    Logistic Regression_Hybrid Sampling (SMOTETome...  
 6    Logistic Regression_Hybrid Sampling (SMOTETome...  
 7    Logistic Regression_Hybrid Sampling (SMOTETome...  
 8    Logistic Regression_Hybrid Sampling (SMOTETome...  
 9    Logistic Regression_Hybrid Sampling (SMOTETome...  
 10   Logistic Regression_Hybrid Sampling (SMOTETome...  
 11   Logistic Regression_Hybrid Sampling (SMOTETome...  
 12   Logistic Regression_Hybrid Sampling (SMOTETome...  
 13   Logistic Regression_Hybrid Sampling (SMOTETome...  
 14   Logistic Regression_Hybrid Sampling (SMOTETome...  
 15   Logistic Regression_Hybrid Sampling (SMOTETome...  
 16   Logistic Regression_Hybrid Sampling (SMOTETome...  
 17   Logistic Regression_Hybrid Sampling (SMOTETome...  
 18   Logistic Regression_Hybrid Sampling (SMOTETome...  
 19   Logistic Regression_Hybrid Sampling (SMOTETome...  
 20   Logistic Regression_Hybrid Sampling (SMOTETome...  
 21   Logistic Regression_Hybrid Sampling (SMOTETome...  
 22   Logistic Regression_Hybrid Sampling (SMOTETome...  
 23   Logistic Regression_Hybrid Sampling (SMOTETome...  
 24   Logistic Regression_Hybrid Sampling (SMOTETome...  
 25   Logistic Regression_Hybrid Sampling (SMOTETome...  
 26   Logistic Regression_Hybrid Sampling (SMOTETome...  
 27   Logistic Regression_Hybrid Sampling (SMOTETome...  
 28   Logistic Regression_Hybrid Sampling (SMOTETome...  
 29   Logistic Regression_Hybrid Sampling (SMOTETome...  
 30   Logistic Regression_Hybrid Sampling (SMOTETome...  
 31   Logistic Regression_Hybrid Sampling (SMOTETome...  
 32   Logistic Regression_Hybrid Sampling (SMOTETome...  
 33   Logistic Regression_Hybrid Sampling (SMOTETome...  
 34   Logistic Regression_Hybrid Sampling (SMOTETome...  
 35   Logistic Regression_Hybrid Sampling (SMOTETome...  
 36   Logistic Regression_Hybrid Sampling (SMOTETome...  
 37   Logistic Regression_Hybrid Sampling (SMOTETome...  
 38   Logistic Regression_Hybrid Sampling (SMOTETome...  
 39   Logistic Regression_Hybrid Sampling (SMOTETome...  
 40   Logistic Regression_Hybrid Sampling (SMOTETome...  
 41   Logistic Regression_Hybrid Sampling (SMOTETome...  
 42   Logistic Regression_Hybrid Sampling (SMOTETome...  
 43   Logistic Regression_Hybrid Sampling (SMOTETome...  
 44   Logistic Regression_Hybrid Sampling (SMOTETome...  
 45   Logistic Regression_Hybrid Sampling (SMOTETome...  
 46   Logistic Regression_Hybrid Sampling (SMOTETome...  
 47   Logistic Regression_Hybrid Sampling (SMOTETome...  
 48   Logistic Regression_Hybrid Sampling (SMOTETome...  
 49   Logistic Regression_Hybrid Sampling (SMOTETome...  
 50   Logistic Regression_Hybrid Sampling (SMOTETome...  
 51   Logistic Regression_Hybrid Sampling (SMOTETome...  
 52   Logistic Regression_Hybrid Sampling (SMOTETome...  
 53   Logistic Regression_Hybrid Sampling (SMOTETome...  
 54   Logistic Regression_Hybrid Sampling (SMOTETome...  
 55   Logistic Regression_Hybrid Sampling (SMOTETome...  
 56   Logistic Regression_Hybrid Sampling (SMOTETome...  
 57   Logistic Regression_Hybrid Sampling (SMOTETome...  
 58   Logistic Regression_Hybrid Sampling (SMOTETome...  
 59   Logistic Regression_Hybrid Sampling (SMOTETome...  
 60   Logistic Regression_Hybrid Sampling (SMOTETome...  
 61   Logistic Regression_Hybrid Sampling (SMOTETome...  
 62   Logistic Regression_Hybrid Sampling (SMOTETome...  
 63   Logistic Regression_Hybrid Sampling (SMOTETome...  
 64   Logistic Regression_Hybrid Sampling (SMOTETome...  
 65   Logistic Regression_Hybrid Sampling (SMOTETome...  
 66   Logistic Regression_Hybrid Sampling (SMOTETome...  
 67   Logistic Regression_Hybrid Sampling (SMOTETome...  
 68   Logistic Regression_Hybrid Sampling (SMOTETome...  
 69   Logistic Regression_Hybrid Sampling (SMOTETome...  
 70   Logistic Regression_Hybrid Sampling (SMOTETome...  
 71   Logistic Regression_Hybrid Sampling (SMOTETome...  
 72   Logistic Regression_Hybrid Sampling (SMOTETome...  
 73   Logistic Regression_Hybrid Sampling (SMOTETome...  
 74   Logistic Regression_Hybrid Sampling (SMOTETome...  
 75   Logistic Regression_Hybrid Sampling (SMOTETome...  
 76   Logistic Regression_Hybrid Sampling (SMOTETome...  
 77   Logistic Regression_Hybrid Sampling (SMOTETome...  
 78   Logistic Regression_Hybrid Sampling (SMOTETome...  
 79   Logistic Regression_Hybrid Sampling (SMOTETome...  
 80   Logistic Regression_Hybrid Sampling (SMOTETome...  
 81   Logistic Regression_Hybrid Sampling (SMOTETome...  
 82   Logistic Regression_Hybrid Sampling (SMOTETome...  
 83   Logistic Regression_Hybrid Sampling (SMOTETome...  
 84   Logistic Regression_Hybrid Sampling (SMOTETome...  
 85   Logistic Regression_Hybrid Sampling (SMOTETome...  
 86   Logistic Regression_Hybrid Sampling (SMOTETome...  
 87   Logistic Regression_Hybrid Sampling (SMOTETome...  
 88   Logistic Regression_Hybrid Sampling (SMOTETome...  
 89   Logistic Regression_Hybrid Sampling (SMOTETome...  
 90   Logistic Regression_Hybrid Sampling (SMOTETome...  
 91   Logistic Regression_Hybrid Sampling (SMOTETome...  
 92   Logistic Regression_Hybrid Sampling (SMOTETome...  
 93   Logistic Regression_Hybrid Sampling (SMOTETome...  
 94   Logistic Regression_Hybrid Sampling (SMOTETome...  
 95   Logistic Regression_Hybrid Sampling (SMOTETome...  
 96   Logistic Regression_Hybrid Sampling (SMOTETome...  
 97   Logistic Regression_Hybrid Sampling (SMOTETome...  
 98   Logistic Regression_Hybrid Sampling (SMOTETome...  
 99   Logistic Regression_Hybrid Sampling (SMOTETome...  
 100  Logistic Regression_Hybrid Sampling (SMOTETome...  
 101  Logistic Regression_Hybrid Sampling (SMOTETome...  
 102  Logistic Regression_Hybrid Sampling (SMOTETome...  
 103  Logistic Regression_Hybrid Sampling (SMOTETome...  
 104  Logistic Regression_Hybrid Sampling (SMOTETome...  
 105  Logistic Regression_Hybrid Sampling (SMOTETome...  
 106  Logistic Regression_Hybrid Sampling (SMOTETome...  
 107  Logistic Regression_Hybrid Sampling (SMOTETome...  
 108  Logistic Regression_Hybrid Sampling (SMOTETome...  
 109  Logistic Regression_Hybrid Sampling (SMOTETome...  ,
                Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Logistic Regression  0.508559             NaN    Median Imputation   
 1    Logistic Regression  0.087177             NaN    Median Imputation   
 2    Logistic Regression  0.725738             NaN    Median Imputation   
 3    Logistic Regression  0.155656             NaN    Median Imputation   
 4    Logistic Regression  0.622130             NaN    Median Imputation   
 5    Logistic Regression  0.222654             NaN    Median Imputation   
 6    Logistic Regression  0.244260             NaN    Median Imputation   
 7    Logistic Regression       NaN             1.0    Median Imputation   
 8    Logistic Regression       NaN            None    Median Imputation   
 9    Logistic Regression       NaN           False    Median Imputation   
 10   Logistic Regression       NaN            True    Median Imputation   
 11   Logistic Regression       NaN               1    Median Imputation   
 12   Logistic Regression       NaN            None    Median Imputation   
 13   Logistic Regression       NaN             100    Median Imputation   
 14   Logistic Regression       NaN            auto    Median Imputation   
 15   Logistic Regression       NaN            None    Median Imputation   
 16   Logistic Regression       NaN              l2    Median Imputation   
 17   Logistic Regression       NaN            None    Median Imputation   
 18   Logistic Regression       NaN           lbfgs    Median Imputation   
 19   Logistic Regression       NaN          0.0001    Median Imputation   
 20   Logistic Regression       NaN               0    Median Imputation   
 21   Logistic Regression       NaN           False    Median Imputation   
 22   Logistic Regression  0.518041             NaN    Median Imputation   
 23   Logistic Regression  0.059259             NaN    Median Imputation   
 24   Logistic Regression  0.682927             NaN    Median Imputation   
 25   Logistic Regression  0.109056             NaN    Median Imputation   
 26   Logistic Regression  0.635181             NaN    Median Imputation   
 27   Logistic Regression  0.222528             NaN    Median Imputation   
 28   Logistic Regression  0.270362             NaN    Median Imputation   
 29   Logistic Regression       NaN             1.0    Median Imputation   
 30   Logistic Regression       NaN            None    Median Imputation   
 31   Logistic Regression       NaN           False    Median Imputation   
 32   Logistic Regression       NaN            True    Median Imputation   
 33   Logistic Regression       NaN               1    Median Imputation   
 34   Logistic Regression       NaN            None    Median Imputation   
 35   Logistic Regression       NaN             100    Median Imputation   
 36   Logistic Regression       NaN            auto    Median Imputation   
 37   Logistic Regression       NaN            None    Median Imputation   
 38   Logistic Regression       NaN              l2    Median Imputation   
 39   Logistic Regression       NaN            None    Median Imputation   
 40   Logistic Regression       NaN           lbfgs    Median Imputation   
 41   Logistic Regression       NaN          0.0001    Median Imputation   
 42   Logistic Regression       NaN               0    Median Imputation   
 43   Logistic Regression       NaN           False    Median Imputation   
 44   Logistic Regression  0.516460             NaN    Median Imputation   
 45   Logistic Regression  0.067192             NaN    Median Imputation   
 46   Logistic Regression  0.684492             NaN    Median Imputation   
 47   Logistic Regression  0.122371             NaN    Median Imputation   
 48   Logistic Regression  0.636101             NaN    Median Imputation   
 49   Logistic Regression  0.209483             NaN    Median Imputation   
 50   Logistic Regression  0.272203             NaN    Median Imputation   
 51   Logistic Regression       NaN             1.0    Median Imputation   
 52   Logistic Regression       NaN            None    Median Imputation   
 53   Logistic Regression       NaN           False    Median Imputation   
 54   Logistic Regression       NaN            True    Median Imputation   
 55   Logistic Regression       NaN               1    Median Imputation   
 56   Logistic Regression       NaN            None    Median Imputation   
 57   Logistic Regression       NaN             100    Median Imputation   
 58   Logistic Regression       NaN            auto    Median Imputation   
 59   Logistic Regression       NaN            None    Median Imputation   
 60   Logistic Regression       NaN              l2    Median Imputation   
 61   Logistic Regression       NaN            None    Median Imputation   
 62   Logistic Regression       NaN           lbfgs    Median Imputation   
 63   Logistic Regression       NaN          0.0001    Median Imputation   
 64   Logistic Regression       NaN               0    Median Imputation   
 65   Logistic Regression       NaN           False    Median Imputation   
 66   Logistic Regression  0.527924             NaN    Median Imputation   
 67   Logistic Regression  0.076433             NaN    Median Imputation   
 68   Logistic Regression  0.734694             NaN    Median Imputation   
 69   Logistic Regression  0.138462             NaN    Median Imputation   
 70   Logistic Regression  0.661445             NaN    Median Imputation   
 71   Logistic Regression  0.266763             NaN    Median Imputation   
 72   Logistic Regression  0.322890             NaN    Median Imputation   
 73   Logistic Regression       NaN             1.0    Median Imputation   
 74   Logistic Regression       NaN            None    Median Imputation   
 75   Logistic Regression       NaN           False    Median Imputation   
 76   Logistic Regression       NaN            True    Median Imputation   
 77   Logistic Regression       NaN               1    Median Imputation   
 78   Logistic Regression       NaN            None    Median Imputation   
 79   Logistic Regression       NaN             100    Median Imputation   
 80   Logistic Regression       NaN            auto    Median Imputation   
 81   Logistic Regression       NaN            None    Median Imputation   
 82   Logistic Regression       NaN              l2    Median Imputation   
 83   Logistic Regression       NaN            None    Median Imputation   
 84   Logistic Regression       NaN           lbfgs    Median Imputation   
 85   Logistic Regression       NaN          0.0001    Median Imputation   
 86   Logistic Regression       NaN               0    Median Imputation   
 87   Logistic Regression       NaN           False    Median Imputation   
 88   Logistic Regression  0.557165             NaN    Median Imputation   
 89   Logistic Regression  0.079748             NaN    Median Imputation   
 90   Logistic Regression  0.643519             NaN    Median Imputation   
 91   Logistic Regression  0.141909             NaN    Median Imputation   
 92   Logistic Regression  0.627634             NaN    Median Imputation   
 93   Logistic Regression  0.211080             NaN    Median Imputation   
 94   Logistic Regression  0.255267             NaN    Median Imputation   
 95   Logistic Regression       NaN             1.0    Median Imputation   
 96   Logistic Regression       NaN            None    Median Imputation   
 97   Logistic Regression       NaN           False    Median Imputation   
 98   Logistic Regression       NaN            True    Median Imputation   
 99   Logistic Regression       NaN               1    Median Imputation   
 100  Logistic Regression       NaN            None    Median Imputation   
 101  Logistic Regression       NaN             100    Median Imputation   
 102  Logistic Regression       NaN            auto    Median Imputation   
 103  Logistic Regression       NaN            None    Median Imputation   
 104  Logistic Regression       NaN              l2    Median Imputation   
 105  Logistic Regression       NaN            None    Median Imputation   
 106  Logistic Regression       NaN           lbfgs    Median Imputation   
 107  Logistic Regression       NaN          0.0001    Median Imputation   
 108  Logistic Regression       NaN               0    Median Imputation   
 109  Logistic Regression       NaN           False    Median Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Logistic Regression_Hybrid Sampling (SMOTETome...  
 1    Logistic Regression_Hybrid Sampling (SMOTETome...  
 2    Logistic Regression_Hybrid Sampling (SMOTETome...  
 3    Logistic Regression_Hybrid Sampling (SMOTETome...  
 4    Logistic Regression_Hybrid Sampling (SMOTETome...  
 5    Logistic Regression_Hybrid Sampling (SMOTETome...  
 6    Logistic Regression_Hybrid Sampling (SMOTETome...  
 7    Logistic Regression_Hybrid Sampling (SMOTETome...  
 8    Logistic Regression_Hybrid Sampling (SMOTETome...  
 9    Logistic Regression_Hybrid Sampling (SMOTETome...  
 10   Logistic Regression_Hybrid Sampling (SMOTETome...  
 11   Logistic Regression_Hybrid Sampling (SMOTETome...  
 12   Logistic Regression_Hybrid Sampling (SMOTETome...  
 13   Logistic Regression_Hybrid Sampling (SMOTETome...  
 14   Logistic Regression_Hybrid Sampling (SMOTETome...  
 15   Logistic Regression_Hybrid Sampling (SMOTETome...  
 16   Logistic Regression_Hybrid Sampling (SMOTETome...  
 17   Logistic Regression_Hybrid Sampling (SMOTETome...  
 18   Logistic Regression_Hybrid Sampling (SMOTETome...  
 19   Logistic Regression_Hybrid Sampling (SMOTETome...  
 20   Logistic Regression_Hybrid Sampling (SMOTETome...  
 21   Logistic Regression_Hybrid Sampling (SMOTETome...  
 22   Logistic Regression_Hybrid Sampling (SMOTETome...  
 23   Logistic Regression_Hybrid Sampling (SMOTETome...  
 24   Logistic Regression_Hybrid Sampling (SMOTETome...  
 25   Logistic Regression_Hybrid Sampling (SMOTETome...  
 26   Logistic Regression_Hybrid Sampling (SMOTETome...  
 27   Logistic Regression_Hybrid Sampling (SMOTETome...  
 28   Logistic Regression_Hybrid Sampling (SMOTETome...  
 29   Logistic Regression_Hybrid Sampling (SMOTETome...  
 30   Logistic Regression_Hybrid Sampling (SMOTETome...  
 31   Logistic Regression_Hybrid Sampling (SMOTETome...  
 32   Logistic Regression_Hybrid Sampling (SMOTETome...  
 33   Logistic Regression_Hybrid Sampling (SMOTETome...  
 34   Logistic Regression_Hybrid Sampling (SMOTETome...  
 35   Logistic Regression_Hybrid Sampling (SMOTETome...  
 36   Logistic Regression_Hybrid Sampling (SMOTETome...  
 37   Logistic Regression_Hybrid Sampling (SMOTETome...  
 38   Logistic Regression_Hybrid Sampling (SMOTETome...  
 39   Logistic Regression_Hybrid Sampling (SMOTETome...  
 40   Logistic Regression_Hybrid Sampling (SMOTETome...  
 41   Logistic Regression_Hybrid Sampling (SMOTETome...  
 42   Logistic Regression_Hybrid Sampling (SMOTETome...  
 43   Logistic Regression_Hybrid Sampling (SMOTETome...  
 44   Logistic Regression_Hybrid Sampling (SMOTETome...  
 45   Logistic Regression_Hybrid Sampling (SMOTETome...  
 46   Logistic Regression_Hybrid Sampling (SMOTETome...  
 47   Logistic Regression_Hybrid Sampling (SMOTETome...  
 48   Logistic Regression_Hybrid Sampling (SMOTETome...  
 49   Logistic Regression_Hybrid Sampling (SMOTETome...  
 50   Logistic Regression_Hybrid Sampling (SMOTETome...  
 51   Logistic Regression_Hybrid Sampling (SMOTETome...  
 52   Logistic Regression_Hybrid Sampling (SMOTETome...  
 53   Logistic Regression_Hybrid Sampling (SMOTETome...  
 54   Logistic Regression_Hybrid Sampling (SMOTETome...  
 55   Logistic Regression_Hybrid Sampling (SMOTETome...  
 56   Logistic Regression_Hybrid Sampling (SMOTETome...  
 57   Logistic Regression_Hybrid Sampling (SMOTETome...  
 58   Logistic Regression_Hybrid Sampling (SMOTETome...  
 59   Logistic Regression_Hybrid Sampling (SMOTETome...  
 60   Logistic Regression_Hybrid Sampling (SMOTETome...  
 61   Logistic Regression_Hybrid Sampling (SMOTETome...  
 62   Logistic Regression_Hybrid Sampling (SMOTETome...  
 63   Logistic Regression_Hybrid Sampling (SMOTETome...  
 64   Logistic Regression_Hybrid Sampling (SMOTETome...  
 65   Logistic Regression_Hybrid Sampling (SMOTETome...  
 66   Logistic Regression_Hybrid Sampling (SMOTETome...  
 67   Logistic Regression_Hybrid Sampling (SMOTETome...  
 68   Logistic Regression_Hybrid Sampling (SMOTETome...  
 69   Logistic Regression_Hybrid Sampling (SMOTETome...  
 70   Logistic Regression_Hybrid Sampling (SMOTETome...  
 71   Logistic Regression_Hybrid Sampling (SMOTETome...  
 72   Logistic Regression_Hybrid Sampling (SMOTETome...  
 73   Logistic Regression_Hybrid Sampling (SMOTETome...  
 74   Logistic Regression_Hybrid Sampling (SMOTETome...  
 75   Logistic Regression_Hybrid Sampling (SMOTETome...  
 76   Logistic Regression_Hybrid Sampling (SMOTETome...  
 77   Logistic Regression_Hybrid Sampling (SMOTETome...  
 78   Logistic Regression_Hybrid Sampling (SMOTETome...  
 79   Logistic Regression_Hybrid Sampling (SMOTETome...  
 80   Logistic Regression_Hybrid Sampling (SMOTETome...  
 81   Logistic Regression_Hybrid Sampling (SMOTETome...  
 82   Logistic Regression_Hybrid Sampling (SMOTETome...  
 83   Logistic Regression_Hybrid Sampling (SMOTETome...  
 84   Logistic Regression_Hybrid Sampling (SMOTETome...  
 85   Logistic Regression_Hybrid Sampling (SMOTETome...  
 86   Logistic Regression_Hybrid Sampling (SMOTETome...  
 87   Logistic Regression_Hybrid Sampling (SMOTETome...  
 88   Logistic Regression_Hybrid Sampling (SMOTETome...  
 89   Logistic Regression_Hybrid Sampling (SMOTETome...  
 90   Logistic Regression_Hybrid Sampling (SMOTETome...  
 91   Logistic Regression_Hybrid Sampling (SMOTETome...  
 92   Logistic Regression_Hybrid Sampling (SMOTETome...  
 93   Logistic Regression_Hybrid Sampling (SMOTETome...  
 94   Logistic Regression_Hybrid Sampling (SMOTETome...  
 95   Logistic Regression_Hybrid Sampling (SMOTETome...  
 96   Logistic Regression_Hybrid Sampling (SMOTETome...  
 97   Logistic Regression_Hybrid Sampling (SMOTETome...  
 98   Logistic Regression_Hybrid Sampling (SMOTETome...  
 99   Logistic Regression_Hybrid Sampling (SMOTETome...  
 100  Logistic Regression_Hybrid Sampling (SMOTETome...  
 101  Logistic Regression_Hybrid Sampling (SMOTETome...  
 102  Logistic Regression_Hybrid Sampling (SMOTETome...  
 103  Logistic Regression_Hybrid Sampling (SMOTETome...  
 104  Logistic Regression_Hybrid Sampling (SMOTETome...  
 105  Logistic Regression_Hybrid Sampling (SMOTETome...  
 106  Logistic Regression_Hybrid Sampling (SMOTETome...  
 107  Logistic Regression_Hybrid Sampling (SMOTETome...  
 108  Logistic Regression_Hybrid Sampling (SMOTETome...  
 109  Logistic Regression_Hybrid Sampling (SMOTETome...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.840927             NaN      Zero Imputation   
 1   k-Nearest Neighbors (k-NN)  0.096703             NaN      Zero Imputation   
 2   k-Nearest Neighbors (k-NN)  0.185654             NaN      Zero Imputation   
 3   k-Nearest Neighbors (k-NN)  0.127168             NaN      Zero Imputation   
 4   k-Nearest Neighbors (k-NN)  0.559796             NaN      Zero Imputation   
 5   k-Nearest Neighbors (k-NN)  0.119271             NaN      Zero Imputation   
 6   k-Nearest Neighbors (k-NN)  0.119593             NaN      Zero Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 15  k-Nearest Neighbors (k-NN)  0.841454             NaN      Zero Imputation   
 16  k-Nearest Neighbors (k-NN)  0.060241             NaN      Zero Imputation   
 17  k-Nearest Neighbors (k-NN)  0.182927             NaN      Zero Imputation   
 18  k-Nearest Neighbors (k-NN)  0.090634             NaN      Zero Imputation   
 19  k-Nearest Neighbors (k-NN)  0.545939             NaN      Zero Imputation   
 20  k-Nearest Neighbors (k-NN)  0.093566             NaN      Zero Imputation   
 21  k-Nearest Neighbors (k-NN)  0.091878             NaN      Zero Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 30  k-Nearest Neighbors (k-NN)  0.838820             NaN      Zero Imputation   
 31  k-Nearest Neighbors (k-NN)  0.048832             NaN      Zero Imputation   
 32  k-Nearest Neighbors (k-NN)  0.122995             NaN      Zero Imputation   
 33  k-Nearest Neighbors (k-NN)  0.069909             NaN      Zero Imputation   
 34  k-Nearest Neighbors (k-NN)  0.530319             NaN      Zero Imputation   
 35  k-Nearest Neighbors (k-NN)  0.071788             NaN      Zero Imputation   
 36  k-Nearest Neighbors (k-NN)  0.060638             NaN      Zero Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 45  k-Nearest Neighbors (k-NN)  0.841412             NaN      Zero Imputation   
 46  k-Nearest Neighbors (k-NN)  0.062500             NaN      Zero Imputation   
 47  k-Nearest Neighbors (k-NN)  0.147959             NaN      Zero Imputation   
 48  k-Nearest Neighbors (k-NN)  0.087879             NaN      Zero Imputation   
 49  k-Nearest Neighbors (k-NN)  0.533114             NaN      Zero Imputation   
 50  k-Nearest Neighbors (k-NN)  0.068946             NaN      Zero Imputation   
 51  k-Nearest Neighbors (k-NN)  0.066229             NaN      Zero Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 60  k-Nearest Neighbors (k-NN)  0.840095             NaN      Zero Imputation   
 61  k-Nearest Neighbors (k-NN)  0.090147             NaN      Zero Imputation   
 62  k-Nearest Neighbors (k-NN)  0.199074             NaN      Zero Imputation   
 63  k-Nearest Neighbors (k-NN)  0.124098             NaN      Zero Imputation   
 64  k-Nearest Neighbors (k-NN)  0.565178             NaN      Zero Imputation   
 65  k-Nearest Neighbors (k-NN)  0.131378             NaN      Zero Imputation   
 66  k-Nearest Neighbors (k-NN)  0.130355             NaN      Zero Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 1   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 2   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 3   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 4   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 5   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 6   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 7   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 8   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 9   k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 10  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 11  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 12  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 13  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 14  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 15  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 16  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 17  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 18  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 19  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 20  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 21  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 22  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 23  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 24  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 25  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 26  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 27  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 28  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 29  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 30  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 31  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 32  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 33  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 34  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 35  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 36  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 37  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 38  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 39  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 40  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 41  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 42  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 43  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 44  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 45  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 46  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 47  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 48  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 49  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 50  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 51  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 52  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 53  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 54  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 55  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 56  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 57  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 58  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 59  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 60  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 61  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 62  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 63  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 64  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 65  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 66  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 67  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 68  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 69  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 70  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 71  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 72  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 73  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  
 74  k-Nearest Neighbors (k-NN)_Oversampling_Zero I...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.839083             NaN      Mean Imputation   
 1   k-Nearest Neighbors (k-NN)  0.069124             NaN      Mean Imputation   
 2   k-Nearest Neighbors (k-NN)  0.126582             NaN      Mean Imputation   
 3   k-Nearest Neighbors (k-NN)  0.089419             NaN      Mean Imputation   
 4   k-Nearest Neighbors (k-NN)  0.545102             NaN      Mean Imputation   
 5   k-Nearest Neighbors (k-NN)  0.099853             NaN      Mean Imputation   
 6   k-Nearest Neighbors (k-NN)  0.090204             NaN      Mean Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 15  k-Nearest Neighbors (k-NN)  0.848038             NaN      Mean Imputation   
 16  k-Nearest Neighbors (k-NN)  0.067086             NaN      Mean Imputation   
 17  k-Nearest Neighbors (k-NN)  0.195122             NaN      Mean Imputation   
 18  k-Nearest Neighbors (k-NN)  0.099844             NaN      Mean Imputation   
 19  k-Nearest Neighbors (k-NN)  0.552346             NaN      Mean Imputation   
 20  k-Nearest Neighbors (k-NN)  0.104619             NaN      Mean Imputation   
 21  k-Nearest Neighbors (k-NN)  0.104692             NaN      Mean Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 30  k-Nearest Neighbors (k-NN)  0.836450             NaN      Mean Imputation   
 31  k-Nearest Neighbors (k-NN)  0.062500             NaN      Mean Imputation   
 32  k-Nearest Neighbors (k-NN)  0.165775             NaN      Mean Imputation   
 33  k-Nearest Neighbors (k-NN)  0.090776             NaN      Mean Imputation   
 34  k-Nearest Neighbors (k-NN)  0.558970             NaN      Mean Imputation   
 35  k-Nearest Neighbors (k-NN)  0.127480             NaN      Mean Imputation   
 36  k-Nearest Neighbors (k-NN)  0.117940             NaN      Mean Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 45  k-Nearest Neighbors (k-NN)  0.841939             NaN      Mean Imputation   
 46  k-Nearest Neighbors (k-NN)  0.062771             NaN      Mean Imputation   
 47  k-Nearest Neighbors (k-NN)  0.147959             NaN      Mean Imputation   
 48  k-Nearest Neighbors (k-NN)  0.088146             NaN      Mean Imputation   
 49  k-Nearest Neighbors (k-NN)  0.539427             NaN      Mean Imputation   
 50  k-Nearest Neighbors (k-NN)  0.085261             NaN      Mean Imputation   
 51  k-Nearest Neighbors (k-NN)  0.078853             NaN      Mean Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 60  k-Nearest Neighbors (k-NN)  0.840095             NaN      Mean Imputation   
 61  k-Nearest Neighbors (k-NN)  0.088421             NaN      Mean Imputation   
 62  k-Nearest Neighbors (k-NN)  0.194444             NaN      Mean Imputation   
 63  k-Nearest Neighbors (k-NN)  0.121563             NaN      Mean Imputation   
 64  k-Nearest Neighbors (k-NN)  0.576130             NaN      Mean Imputation   
 65  k-Nearest Neighbors (k-NN)  0.158199             NaN      Mean Imputation   
 66  k-Nearest Neighbors (k-NN)  0.152259             NaN      Mean Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 1   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 2   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 3   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 4   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 5   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 6   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 7   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 8   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 9   k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 10  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 11  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 12  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 13  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 14  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 15  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 16  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 17  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 18  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 19  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 20  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 21  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 22  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 23  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 24  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 25  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 26  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 27  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 28  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 29  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 30  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 31  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 32  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 33  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 34  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 35  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 36  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 37  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 38  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 39  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 40  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 41  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 42  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 43  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 44  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 45  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 46  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 47  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 48  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 49  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 50  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 51  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 52  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 53  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 54  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 55  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 56  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 57  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 58  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 59  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 60  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 61  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 62  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 63  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 64  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 65  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 66  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 67  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 68  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 69  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 70  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 71  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 72  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 73  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  
 74  k-Nearest Neighbors (k-NN)_Oversampling_Mean I...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.841454             NaN    Median Imputation   
 1   k-Nearest Neighbors (k-NN)  0.088036             NaN    Median Imputation   
 2   k-Nearest Neighbors (k-NN)  0.164557             NaN    Median Imputation   
 3   k-Nearest Neighbors (k-NN)  0.114706             NaN    Median Imputation   
 4   k-Nearest Neighbors (k-NN)  0.554976             NaN    Median Imputation   
 5   k-Nearest Neighbors (k-NN)  0.111955             NaN    Median Imputation   
 6   k-Nearest Neighbors (k-NN)  0.109951             NaN    Median Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 15  k-Nearest Neighbors (k-NN)  0.843034             NaN    Median Imputation   
 16  k-Nearest Neighbors (k-NN)  0.066265             NaN    Median Imputation   
 17  k-Nearest Neighbors (k-NN)  0.201220             NaN    Median Imputation   
 18  k-Nearest Neighbors (k-NN)  0.099698             NaN    Median Imputation   
 19  k-Nearest Neighbors (k-NN)  0.551658             NaN    Median Imputation   
 20  k-Nearest Neighbors (k-NN)  0.107138             NaN    Median Imputation   
 21  k-Nearest Neighbors (k-NN)  0.103316             NaN    Median Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 30  k-Nearest Neighbors (k-NN)  0.839083             NaN    Median Imputation   
 31  k-Nearest Neighbors (k-NN)  0.058333             NaN    Median Imputation   
 32  k-Nearest Neighbors (k-NN)  0.149733             NaN    Median Imputation   
 33  k-Nearest Neighbors (k-NN)  0.083958             NaN    Median Imputation   
 34  k-Nearest Neighbors (k-NN)  0.541180             NaN    Median Imputation   
 35  k-Nearest Neighbors (k-NN)  0.090324             NaN    Median Imputation   
 36  k-Nearest Neighbors (k-NN)  0.082360             NaN    Median Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 45  k-Nearest Neighbors (k-NN)  0.841939             NaN    Median Imputation   
 46  k-Nearest Neighbors (k-NN)  0.053097             NaN    Median Imputation   
 47  k-Nearest Neighbors (k-NN)  0.122449             NaN    Median Imputation   
 48  k-Nearest Neighbors (k-NN)  0.074074             NaN    Median Imputation   
 49  k-Nearest Neighbors (k-NN)  0.539729             NaN    Median Imputation   
 50  k-Nearest Neighbors (k-NN)  0.088141             NaN    Median Imputation   
 51  k-Nearest Neighbors (k-NN)  0.079459             NaN    Median Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 60  k-Nearest Neighbors (k-NN)  0.844310             NaN    Median Imputation   
 61  k-Nearest Neighbors (k-NN)  0.098501             NaN    Median Imputation   
 62  k-Nearest Neighbors (k-NN)  0.212963             NaN    Median Imputation   
 63  k-Nearest Neighbors (k-NN)  0.134700             NaN    Median Imputation   
 64  k-Nearest Neighbors (k-NN)  0.576763             NaN    Median Imputation   
 65  k-Nearest Neighbors (k-NN)  0.155364             NaN    Median Imputation   
 66  k-Nearest Neighbors (k-NN)  0.153525             NaN    Median Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 1   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 2   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 3   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 4   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 5   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 6   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 7   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 8   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 9   k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 10  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 11  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 12  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 13  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 14  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 15  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 16  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 17  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 18  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 19  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 20  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 21  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 22  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 23  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 24  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 25  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 26  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 27  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 28  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 29  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 30  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 31  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 32  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 33  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 34  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 35  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 36  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 37  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 38  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 39  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 40  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 41  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 42  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 43  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 44  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 45  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 46  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 47  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 48  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 49  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 50  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 51  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 52  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 53  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 54  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 55  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 56  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 57  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 58  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 59  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 60  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 61  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 62  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 63  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 64  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 65  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 66  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 67  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 68  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 69  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 70  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 71  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 72  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 73  k-Nearest Neighbors (k-NN)_Oversampling_Median...  
 74  k-Nearest Neighbors (k-NN)_Oversampling_Median...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.621543             NaN      Zero Imputation   
 1   k-Nearest Neighbors (k-NN)  0.088477             NaN      Zero Imputation   
 2   k-Nearest Neighbors (k-NN)  0.544304             NaN      Zero Imputation   
 3   k-Nearest Neighbors (k-NN)  0.152212             NaN      Zero Imputation   
 4   k-Nearest Neighbors (k-NN)  0.617164             NaN      Zero Imputation   
 5   k-Nearest Neighbors (k-NN)  0.170989             NaN      Zero Imputation   
 6   k-Nearest Neighbors (k-NN)  0.234329             NaN      Zero Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 15  k-Nearest Neighbors (k-NN)  0.616539             NaN      Zero Imputation   
 16  k-Nearest Neighbors (k-NN)  0.071618             NaN      Zero Imputation   
 17  k-Nearest Neighbors (k-NN)  0.658537             NaN      Zero Imputation   
 18  k-Nearest Neighbors (k-NN)  0.129187             NaN      Zero Imputation   
 19  k-Nearest Neighbors (k-NN)  0.658879             NaN      Zero Imputation   
 20  k-Nearest Neighbors (k-NN)  0.273180             NaN      Zero Imputation   
 21  k-Nearest Neighbors (k-NN)  0.317758             NaN      Zero Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 30  k-Nearest Neighbors (k-NN)  0.618120             NaN      Zero Imputation   
 31  k-Nearest Neighbors (k-NN)  0.066575             NaN      Zero Imputation   
 32  k-Nearest Neighbors (k-NN)  0.518717             NaN      Zero Imputation   
 33  k-Nearest Neighbors (k-NN)  0.118005             NaN      Zero Imputation   
 34  k-Nearest Neighbors (k-NN)  0.601634             NaN      Zero Imputation   
 35  k-Nearest Neighbors (k-NN)  0.171524             NaN      Zero Imputation   
 36  k-Nearest Neighbors (k-NN)  0.203268             NaN      Zero Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 45  k-Nearest Neighbors (k-NN)  0.599579             NaN      Zero Imputation   
 46  k-Nearest Neighbors (k-NN)  0.067320             NaN      Zero Imputation   
 47  k-Nearest Neighbors (k-NN)  0.525510             NaN      Zero Imputation   
 48  k-Nearest Neighbors (k-NN)  0.119351             NaN      Zero Imputation   
 49  k-Nearest Neighbors (k-NN)  0.586742             NaN      Zero Imputation   
 50  k-Nearest Neighbors (k-NN)  0.129121             NaN      Zero Imputation   
 51  k-Nearest Neighbors (k-NN)  0.173484             NaN      Zero Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 60  k-Nearest Neighbors (k-NN)  0.608535             NaN      Zero Imputation   
 61  k-Nearest Neighbors (k-NN)  0.081686             NaN      Zero Imputation   
 62  k-Nearest Neighbors (k-NN)  0.574074             NaN      Zero Imputation   
 63  k-Nearest Neighbors (k-NN)  0.143022             NaN      Zero Imputation   
 64  k-Nearest Neighbors (k-NN)  0.619156             NaN      Zero Imputation   
 65  k-Nearest Neighbors (k-NN)  0.184689             NaN      Zero Imputation   
 66  k-Nearest Neighbors (k-NN)  0.238312             NaN      Zero Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 1   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 2   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 3   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 4   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 5   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 6   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 7   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 8   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 9   k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 10  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 11  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 12  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 13  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 14  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 15  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 16  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 17  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 18  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 19  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 20  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 21  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 22  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 23  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 24  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 25  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 26  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 27  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 28  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 29  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 30  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 31  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 32  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 33  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 34  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 35  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 36  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 37  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 38  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 39  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 40  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 41  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 42  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 43  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 44  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 45  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 46  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 47  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 48  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 49  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 50  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 51  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 52  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 53  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 54  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 55  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 56  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 57  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 58  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 59  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 60  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 61  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 62  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 63  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 64  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 65  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 66  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 67  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 68  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 69  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 70  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 71  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 72  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 73  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  
 74  k-Nearest Neighbors (k-NN)_Undersampling_Zero ...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.610482             NaN      Mean Imputation   
 1   k-Nearest Neighbors (k-NN)  0.080972             NaN      Mean Imputation   
 2   k-Nearest Neighbors (k-NN)  0.506329             NaN      Mean Imputation   
 3   k-Nearest Neighbors (k-NN)  0.139616             NaN      Mean Imputation   
 4   k-Nearest Neighbors (k-NN)  0.591465             NaN      Mean Imputation   
 5   k-Nearest Neighbors (k-NN)  0.123745             NaN      Mean Imputation   
 6   k-Nearest Neighbors (k-NN)  0.182929             NaN      Mean Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 15  k-Nearest Neighbors (k-NN)  0.590730             NaN      Mean Imputation   
 16  k-Nearest Neighbors (k-NN)  0.057888             NaN      Mean Imputation   
 17  k-Nearest Neighbors (k-NN)  0.554878             NaN      Mean Imputation   
 18  k-Nearest Neighbors (k-NN)  0.104839             NaN      Mean Imputation   
 19  k-Nearest Neighbors (k-NN)  0.603360             NaN      Mean Imputation   
 20  k-Nearest Neighbors (k-NN)  0.147226             NaN      Mean Imputation   
 21  k-Nearest Neighbors (k-NN)  0.206720             NaN      Mean Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 30  k-Nearest Neighbors (k-NN)  0.570714             NaN      Mean Imputation   
 31  k-Nearest Neighbors (k-NN)  0.070280             NaN      Mean Imputation   
 32  k-Nearest Neighbors (k-NN)  0.631016             NaN      Mean Imputation   
 33  k-Nearest Neighbors (k-NN)  0.126474             NaN      Mean Imputation   
 34  k-Nearest Neighbors (k-NN)  0.613856             NaN      Mean Imputation   
 35  k-Nearest Neighbors (k-NN)  0.198606             NaN      Mean Imputation   
 36  k-Nearest Neighbors (k-NN)  0.227711             NaN      Mean Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 45  k-Nearest Neighbors (k-NN)  0.626185             NaN      Mean Imputation   
 46  k-Nearest Neighbors (k-NN)  0.072676             NaN      Mean Imputation   
 47  k-Nearest Neighbors (k-NN)  0.530612             NaN      Mean Imputation   
 48  k-Nearest Neighbors (k-NN)  0.127843             NaN      Mean Imputation   
 49  k-Nearest Neighbors (k-NN)  0.601657             NaN      Mean Imputation   
 50  k-Nearest Neighbors (k-NN)  0.162001             NaN      Mean Imputation   
 51  k-Nearest Neighbors (k-NN)  0.203313             NaN      Mean Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 60  k-Nearest Neighbors (k-NN)  0.603003             NaN      Mean Imputation   
 61  k-Nearest Neighbors (k-NN)  0.081116             NaN      Mean Imputation   
 62  k-Nearest Neighbors (k-NN)  0.578704             NaN      Mean Imputation   
 63  k-Nearest Neighbors (k-NN)  0.142288             NaN      Mean Imputation   
 64  k-Nearest Neighbors (k-NN)  0.621237             NaN      Mean Imputation   
 65  k-Nearest Neighbors (k-NN)  0.183173             NaN      Mean Imputation   
 66  k-Nearest Neighbors (k-NN)  0.242474             NaN      Mean Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 1   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 2   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 3   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 4   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 5   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 6   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 7   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 8   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 9   k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 10  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 11  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 12  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 13  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 14  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 15  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 16  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 17  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 18  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 19  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 20  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 21  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 22  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 23  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 24  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 25  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 26  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 27  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 28  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 29  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 30  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 31  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 32  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 33  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 34  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 35  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 36  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 37  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 38  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 39  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 40  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 41  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 42  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 43  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 44  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 45  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 46  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 47  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 48  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 49  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 50  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 51  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 52  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 53  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 54  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 55  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 56  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 57  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 58  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 59  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 60  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 61  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 62  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 63  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 64  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 65  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 66  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 67  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 68  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 69  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 70  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 71  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 72  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 73  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  
 74  k-Nearest Neighbors (k-NN)_Undersampling_Mean ...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.611799             NaN    Median Imputation   
 1   k-Nearest Neighbors (k-NN)  0.081812             NaN    Median Imputation   
 2   k-Nearest Neighbors (k-NN)  0.510549             NaN    Median Imputation   
 3   k-Nearest Neighbors (k-NN)  0.141026             NaN    Median Imputation   
 4   k-Nearest Neighbors (k-NN)  0.586476             NaN    Median Imputation   
 5   k-Nearest Neighbors (k-NN)  0.129088             NaN    Median Imputation   
 6   k-Nearest Neighbors (k-NN)  0.172952             NaN    Median Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 15  k-Nearest Neighbors (k-NN)  0.600474             NaN    Median Imputation   
 16  k-Nearest Neighbors (k-NN)  0.057554             NaN    Median Imputation   
 17  k-Nearest Neighbors (k-NN)  0.536585             NaN    Median Imputation   
 18  k-Nearest Neighbors (k-NN)  0.103957             NaN    Median Imputation   
 19  k-Nearest Neighbors (k-NN)  0.609024             NaN    Median Imputation   
 20  k-Nearest Neighbors (k-NN)  0.139943             NaN    Median Imputation   
 21  k-Nearest Neighbors (k-NN)  0.218049             NaN    Median Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 30  k-Nearest Neighbors (k-NN)  0.595997             NaN    Median Imputation   
 31  k-Nearest Neighbors (k-NN)  0.071837             NaN    Median Imputation   
 32  k-Nearest Neighbors (k-NN)  0.604278             NaN    Median Imputation   
 33  k-Nearest Neighbors (k-NN)  0.128409             NaN    Median Imputation   
 34  k-Nearest Neighbors (k-NN)  0.624269             NaN    Median Imputation   
 35  k-Nearest Neighbors (k-NN)  0.199846             NaN    Median Imputation   
 36  k-Nearest Neighbors (k-NN)  0.248537             NaN    Median Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 45  k-Nearest Neighbors (k-NN)  0.611433             NaN    Median Imputation   
 46  k-Nearest Neighbors (k-NN)  0.074518             NaN    Median Imputation   
 47  k-Nearest Neighbors (k-NN)  0.571429             NaN    Median Imputation   
 48  k-Nearest Neighbors (k-NN)  0.131842             NaN    Median Imputation   
 49  k-Nearest Neighbors (k-NN)  0.623647             NaN    Median Imputation   
 50  k-Nearest Neighbors (k-NN)  0.191514             NaN    Median Imputation   
 51  k-Nearest Neighbors (k-NN)  0.247293             NaN    Median Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 60  k-Nearest Neighbors (k-NN)  0.610379             NaN    Median Imputation   
 61  k-Nearest Neighbors (k-NN)  0.076459             NaN    Median Imputation   
 62  k-Nearest Neighbors (k-NN)  0.527778             NaN    Median Imputation   
 63  k-Nearest Neighbors (k-NN)  0.133568             NaN    Median Imputation   
 64  k-Nearest Neighbors (k-NN)  0.584662             NaN    Median Imputation   
 65  k-Nearest Neighbors (k-NN)  0.143141             NaN    Median Imputation   
 66  k-Nearest Neighbors (k-NN)  0.169324             NaN    Median Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 1   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 2   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 3   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 4   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 5   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 6   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 7   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 8   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 9   k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 10  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 11  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 12  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 13  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 14  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 15  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 16  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 17  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 18  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 19  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 20  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 21  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 22  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 23  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 24  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 25  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 26  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 27  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 28  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 29  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 30  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 31  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 32  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 33  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 34  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 35  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 36  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 37  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 38  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 39  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 40  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 41  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 42  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 43  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 44  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 45  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 46  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 47  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 48  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 49  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 50  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 51  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 52  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 53  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 54  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 55  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 56  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 57  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 58  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 59  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 60  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 61  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 62  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 63  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 64  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 65  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 66  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 67  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 68  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 69  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 70  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 71  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 72  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 73  k-Nearest Neighbors (k-NN)_Undersampling_Media...  
 74  k-Nearest Neighbors (k-NN)_Undersampling_Media...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.672110             NaN      Zero Imputation   
 1   k-Nearest Neighbors (k-NN)  0.081395             NaN      Zero Imputation   
 2   k-Nearest Neighbors (k-NN)  0.413502             NaN      Zero Imputation   
 3   k-Nearest Neighbors (k-NN)  0.136017             NaN      Zero Imputation   
 4   k-Nearest Neighbors (k-NN)  0.576071             NaN      Zero Imputation   
 5   k-Nearest Neighbors (k-NN)  0.122734             NaN      Zero Imputation   
 6   k-Nearest Neighbors (k-NN)  0.152142             NaN      Zero Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 15  k-Nearest Neighbors (k-NN)  0.648670             NaN      Zero Imputation   
 16  k-Nearest Neighbors (k-NN)  0.056818             NaN      Zero Imputation   
 17  k-Nearest Neighbors (k-NN)  0.457317             NaN      Zero Imputation   
 18  k-Nearest Neighbors (k-NN)  0.101078             NaN      Zero Imputation   
 19  k-Nearest Neighbors (k-NN)  0.572238             NaN      Zero Imputation   
 20  k-Nearest Neighbors (k-NN)  0.122920             NaN      Zero Imputation   
 21  k-Nearest Neighbors (k-NN)  0.144475             NaN      Zero Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 30  k-Nearest Neighbors (k-NN)  0.652884             NaN      Zero Imputation   
 31  k-Nearest Neighbors (k-NN)  0.057858             NaN      Zero Imputation   
 32  k-Nearest Neighbors (k-NN)  0.395722             NaN      Zero Imputation   
 33  k-Nearest Neighbors (k-NN)  0.100955             NaN      Zero Imputation   
 34  k-Nearest Neighbors (k-NN)  0.537124             NaN      Zero Imputation   
 35  k-Nearest Neighbors (k-NN)  0.089229             NaN      Zero Imputation   
 36  k-Nearest Neighbors (k-NN)  0.074247             NaN      Zero Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 45  k-Nearest Neighbors (k-NN)  0.653583             NaN      Zero Imputation   
 46  k-Nearest Neighbors (k-NN)  0.061176             NaN      Zero Imputation   
 47  k-Nearest Neighbors (k-NN)  0.397959             NaN      Zero Imputation   
 48  k-Nearest Neighbors (k-NN)  0.106050             NaN      Zero Imputation   
 49  k-Nearest Neighbors (k-NN)  0.527472             NaN      Zero Imputation   
 50  k-Nearest Neighbors (k-NN)  0.065459             NaN      Zero Imputation   
 51  k-Nearest Neighbors (k-NN)  0.054943             NaN      Zero Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 60  k-Nearest Neighbors (k-NN)  0.655163             NaN      Zero Imputation   
 61  k-Nearest Neighbors (k-NN)  0.077340             NaN      Zero Imputation   
 62  k-Nearest Neighbors (k-NN)  0.462963             NaN      Zero Imputation   
 63  k-Nearest Neighbors (k-NN)  0.132538             NaN      Zero Imputation   
 64  k-Nearest Neighbors (k-NN)  0.574187             NaN      Zero Imputation   
 65  k-Nearest Neighbors (k-NN)  0.134182             NaN      Zero Imputation   
 66  k-Nearest Neighbors (k-NN)  0.148374             NaN      Zero Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.687385             NaN      Mean Imputation   
 1   k-Nearest Neighbors (k-NN)  0.078901             NaN      Mean Imputation   
 2   k-Nearest Neighbors (k-NN)  0.375527             NaN      Mean Imputation   
 3   k-Nearest Neighbors (k-NN)  0.130403             NaN      Mean Imputation   
 4   k-Nearest Neighbors (k-NN)  0.541897             NaN      Mean Imputation   
 5   k-Nearest Neighbors (k-NN)  0.083673             NaN      Mean Imputation   
 6   k-Nearest Neighbors (k-NN)  0.083793             NaN      Mean Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 15  k-Nearest Neighbors (k-NN)  0.666842             NaN      Mean Imputation   
 16  k-Nearest Neighbors (k-NN)  0.059247             NaN      Mean Imputation   
 17  k-Nearest Neighbors (k-NN)  0.451220             NaN      Mean Imputation   
 18  k-Nearest Neighbors (k-NN)  0.104742             NaN      Mean Imputation   
 19  k-Nearest Neighbors (k-NN)  0.559195             NaN      Mean Imputation   
 20  k-Nearest Neighbors (k-NN)  0.128118             NaN      Mean Imputation   
 21  k-Nearest Neighbors (k-NN)  0.118390             NaN      Mean Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 30  k-Nearest Neighbors (k-NN)  0.667369             NaN      Mean Imputation   
 31  k-Nearest Neighbors (k-NN)  0.068910             NaN      Mean Imputation   
 32  k-Nearest Neighbors (k-NN)  0.459893             NaN      Mean Imputation   
 33  k-Nearest Neighbors (k-NN)  0.119861             NaN      Mean Imputation   
 34  k-Nearest Neighbors (k-NN)  0.570259             NaN      Mean Imputation   
 35  k-Nearest Neighbors (k-NN)  0.143175             NaN      Mean Imputation   
 36  k-Nearest Neighbors (k-NN)  0.140517             NaN      Mean Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 45  k-Nearest Neighbors (k-NN)  0.687302             NaN      Mean Imputation   
 46  k-Nearest Neighbors (k-NN)  0.066492             NaN      Mean Imputation   
 47  k-Nearest Neighbors (k-NN)  0.387755             NaN      Mean Imputation   
 48  k-Nearest Neighbors (k-NN)  0.113518             NaN      Mean Imputation   
 49  k-Nearest Neighbors (k-NN)  0.552273             NaN      Mean Imputation   
 50  k-Nearest Neighbors (k-NN)  0.091366             NaN      Mean Imputation   
 51  k-Nearest Neighbors (k-NN)  0.104545             NaN      Mean Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 60  k-Nearest Neighbors (k-NN)  0.666754             NaN      Mean Imputation   
 61  k-Nearest Neighbors (k-NN)  0.072535             NaN      Mean Imputation   
 62  k-Nearest Neighbors (k-NN)  0.412037             NaN      Mean Imputation   
 63  k-Nearest Neighbors (k-NN)  0.123354             NaN      Mean Imputation   
 64  k-Nearest Neighbors (k-NN)  0.568240             NaN      Mean Imputation   
 65  k-Nearest Neighbors (k-NN)  0.110392             NaN      Mean Imputation   
 66  k-Nearest Neighbors (k-NN)  0.136480             NaN      Mean Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.671846             NaN    Median Imputation   
 1   k-Nearest Neighbors (k-NN)  0.077117             NaN    Median Imputation   
 2   k-Nearest Neighbors (k-NN)  0.388186             NaN    Median Imputation   
 3   k-Nearest Neighbors (k-NN)  0.128671             NaN    Median Imputation   
 4   k-Nearest Neighbors (k-NN)  0.544383             NaN    Median Imputation   
 5   k-Nearest Neighbors (k-NN)  0.096267             NaN    Median Imputation   
 6   k-Nearest Neighbors (k-NN)  0.088766             NaN    Median Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 15  k-Nearest Neighbors (k-NN)  0.652620             NaN    Median Imputation   
 16  k-Nearest Neighbors (k-NN)  0.054742             NaN    Median Imputation   
 17  k-Nearest Neighbors (k-NN)  0.432927             NaN    Median Imputation   
 18  k-Nearest Neighbors (k-NN)  0.097194             NaN    Median Imputation   
 19  k-Nearest Neighbors (k-NN)  0.567693             NaN    Median Imputation   
 20  k-Nearest Neighbors (k-NN)  0.122789             NaN    Median Imputation   
 21  k-Nearest Neighbors (k-NN)  0.135387             NaN    Median Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 30  k-Nearest Neighbors (k-NN)  0.661838             NaN    Median Imputation   
 31  k-Nearest Neighbors (k-NN)  0.063644             NaN    Median Imputation   
 32  k-Nearest Neighbors (k-NN)  0.427807             NaN    Median Imputation   
 33  k-Nearest Neighbors (k-NN)  0.110803             NaN    Median Imputation   
 34  k-Nearest Neighbors (k-NN)  0.561666             NaN    Median Imputation   
 35  k-Nearest Neighbors (k-NN)  0.119676             NaN    Median Imputation   
 36  k-Nearest Neighbors (k-NN)  0.123332             NaN    Median Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 45  k-Nearest Neighbors (k-NN)  0.659905             NaN    Median Imputation   
 46  k-Nearest Neighbors (k-NN)  0.065131             NaN    Median Imputation   
 47  k-Nearest Neighbors (k-NN)  0.418367             NaN    Median Imputation   
 48  k-Nearest Neighbors (k-NN)  0.112715             NaN    Median Imputation   
 49  k-Nearest Neighbors (k-NN)  0.550923             NaN    Median Imputation   
 50  k-Nearest Neighbors (k-NN)  0.097574             NaN    Median Imputation   
 51  k-Nearest Neighbors (k-NN)  0.101845             NaN    Median Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 60  k-Nearest Neighbors (k-NN)  0.670443             NaN    Median Imputation   
 61  k-Nearest Neighbors (k-NN)  0.078240             NaN    Median Imputation   
 62  k-Nearest Neighbors (k-NN)  0.444444             NaN    Median Imputation   
 63  k-Nearest Neighbors (k-NN)  0.133056             NaN    Median Imputation   
 64  k-Nearest Neighbors (k-NN)  0.576799             NaN    Median Imputation   
 65  k-Nearest Neighbors (k-NN)  0.128523             NaN    Median Imputation   
 66  k-Nearest Neighbors (k-NN)  0.153598             NaN    Median Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.747169             NaN      Zero Imputation   
 1   k-Nearest Neighbors (k-NN)  0.089671             NaN      Zero Imputation   
 2   k-Nearest Neighbors (k-NN)  0.333333             NaN      Zero Imputation   
 3   k-Nearest Neighbors (k-NN)  0.141324             NaN      Zero Imputation   
 4   k-Nearest Neighbors (k-NN)  0.567490             NaN      Zero Imputation   
 5   k-Nearest Neighbors (k-NN)  0.115785             NaN      Zero Imputation   
 6   k-Nearest Neighbors (k-NN)  0.134980             NaN      Zero Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 15  k-Nearest Neighbors (k-NN)  0.741638             NaN      Zero Imputation   
 16  k-Nearest Neighbors (k-NN)  0.051592             NaN      Zero Imputation   
 17  k-Nearest Neighbors (k-NN)  0.286585             NaN      Zero Imputation   
 18  k-Nearest Neighbors (k-NN)  0.087442             NaN      Zero Imputation   
 19  k-Nearest Neighbors (k-NN)  0.549695             NaN      Zero Imputation   
 20  k-Nearest Neighbors (k-NN)  0.105746             NaN      Zero Imputation   
 21  k-Nearest Neighbors (k-NN)  0.099390             NaN      Zero Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 30  k-Nearest Neighbors (k-NN)  0.746379             NaN      Zero Imputation   
 31  k-Nearest Neighbors (k-NN)  0.058087             NaN      Zero Imputation   
 32  k-Nearest Neighbors (k-NN)  0.272727             NaN      Zero Imputation   
 33  k-Nearest Neighbors (k-NN)  0.095775             NaN      Zero Imputation   
 34  k-Nearest Neighbors (k-NN)  0.537049             NaN      Zero Imputation   
 35  k-Nearest Neighbors (k-NN)  0.070071             NaN      Zero Imputation   
 36  k-Nearest Neighbors (k-NN)  0.074099             NaN      Zero Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 45  k-Nearest Neighbors (k-NN)  0.742097             NaN      Zero Imputation   
 46  k-Nearest Neighbors (k-NN)  0.055619             NaN      Zero Imputation   
 47  k-Nearest Neighbors (k-NN)  0.250000             NaN      Zero Imputation   
 48  k-Nearest Neighbors (k-NN)  0.090994             NaN      Zero Imputation   
 49  k-Nearest Neighbors (k-NN)  0.535064             NaN      Zero Imputation   
 50  k-Nearest Neighbors (k-NN)  0.062880             NaN      Zero Imputation   
 51  k-Nearest Neighbors (k-NN)  0.070129             NaN      Zero Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 60  k-Nearest Neighbors (k-NN)  0.747629             NaN      Zero Imputation   
 61  k-Nearest Neighbors (k-NN)  0.077449             NaN      Zero Imputation   
 62  k-Nearest Neighbors (k-NN)  0.314815             NaN      Zero Imputation   
 63  k-Nearest Neighbors (k-NN)  0.124314             NaN      Zero Imputation   
 64  k-Nearest Neighbors (k-NN)  0.567306             NaN      Zero Imputation   
 65  k-Nearest Neighbors (k-NN)  0.123831             NaN      Zero Imputation   
 66  k-Nearest Neighbors (k-NN)  0.134611             NaN      Zero Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Zero Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Zero Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Zero Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Zero Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Zero Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Zero Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Zero Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.761917             NaN      Mean Imputation   
 1   k-Nearest Neighbors (k-NN)  0.072983             NaN      Mean Imputation   
 2   k-Nearest Neighbors (k-NN)  0.240506             NaN      Mean Imputation   
 3   k-Nearest Neighbors (k-NN)  0.111984             NaN      Mean Imputation   
 4   k-Nearest Neighbors (k-NN)  0.541348             NaN      Mean Imputation   
 5   k-Nearest Neighbors (k-NN)  0.081288             NaN      Mean Imputation   
 6   k-Nearest Neighbors (k-NN)  0.082696             NaN      Mean Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 15  k-Nearest Neighbors (k-NN)  0.760600             NaN      Mean Imputation   
 16  k-Nearest Neighbors (k-NN)  0.060213             NaN      Mean Imputation   
 17  k-Nearest Neighbors (k-NN)  0.310976             NaN      Mean Imputation   
 18  k-Nearest Neighbors (k-NN)  0.100890             NaN      Mean Imputation   
 19  k-Nearest Neighbors (k-NN)  0.556115             NaN      Mean Imputation   
 20  k-Nearest Neighbors (k-NN)  0.092544             NaN      Mean Imputation   
 21  k-Nearest Neighbors (k-NN)  0.112230             NaN      Mean Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 30  k-Nearest Neighbors (k-NN)  0.750329             NaN      Mean Imputation   
 31  k-Nearest Neighbors (k-NN)  0.070056             NaN      Mean Imputation   
 32  k-Nearest Neighbors (k-NN)  0.331551             NaN      Mean Imputation   
 33  k-Nearest Neighbors (k-NN)  0.115672             NaN      Mean Imputation   
 34  k-Nearest Neighbors (k-NN)  0.567855             NaN      Mean Imputation   
 35  k-Nearest Neighbors (k-NN)  0.125149             NaN      Mean Imputation   
 36  k-Nearest Neighbors (k-NN)  0.135710             NaN      Mean Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 45  k-Nearest Neighbors (k-NN)  0.769231             NaN      Mean Imputation   
 46  k-Nearest Neighbors (k-NN)  0.073935             NaN      Mean Imputation   
 47  k-Nearest Neighbors (k-NN)  0.301020             NaN      Mean Imputation   
 48  k-Nearest Neighbors (k-NN)  0.118712             NaN      Mean Imputation   
 49  k-Nearest Neighbors (k-NN)  0.558555             NaN      Mean Imputation   
 50  k-Nearest Neighbors (k-NN)  0.112432             NaN      Mean Imputation   
 51  k-Nearest Neighbors (k-NN)  0.117110             NaN      Mean Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 60  k-Nearest Neighbors (k-NN)  0.755005             NaN      Mean Imputation   
 61  k-Nearest Neighbors (k-NN)  0.072967             NaN      Mean Imputation   
 62  k-Nearest Neighbors (k-NN)  0.282407             NaN      Mean Imputation   
 63  k-Nearest Neighbors (k-NN)  0.115970             NaN      Mean Imputation   
 64  k-Nearest Neighbors (k-NN)  0.561117             NaN      Mean Imputation   
 65  k-Nearest Neighbors (k-NN)  0.112813             NaN      Mean Imputation   
 66  k-Nearest Neighbors (k-NN)  0.122234             NaN      Mean Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto      Mean Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30      Mean Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski      Mean Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None      Mean Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5      Mean Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2      Mean Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform      Mean Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                      Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   k-Nearest Neighbors (k-NN)  0.752436             NaN    Median Imputation   
 1   k-Nearest Neighbors (k-NN)  0.080048             NaN    Median Imputation   
 2   k-Nearest Neighbors (k-NN)  0.282700             NaN    Median Imputation   
 3   k-Nearest Neighbors (k-NN)  0.124767             NaN    Median Imputation   
 4   k-Nearest Neighbors (k-NN)  0.552447             NaN    Median Imputation   
 5   k-Nearest Neighbors (k-NN)  0.093414             NaN    Median Imputation   
 6   k-Nearest Neighbors (k-NN)  0.104894             NaN    Median Imputation   
 7   k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 8   k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 9   k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 10  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 11  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 12  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 13  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 14  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 15  k-Nearest Neighbors (k-NN)  0.746379             NaN    Median Imputation   
 16  k-Nearest Neighbors (k-NN)  0.053631             NaN    Median Imputation   
 17  k-Nearest Neighbors (k-NN)  0.292683             NaN    Median Imputation   
 18  k-Nearest Neighbors (k-NN)  0.090652             NaN    Median Imputation   
 19  k-Nearest Neighbors (k-NN)  0.540539             NaN    Median Imputation   
 20  k-Nearest Neighbors (k-NN)  0.073481             NaN    Median Imputation   
 21  k-Nearest Neighbors (k-NN)  0.081078             NaN    Median Imputation   
 22  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 23  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 24  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 25  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 26  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 27  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 28  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 29  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 30  k-Nearest Neighbors (k-NN)  0.748222             NaN    Median Imputation   
 31  k-Nearest Neighbors (k-NN)  0.061574             NaN    Median Imputation   
 32  k-Nearest Neighbors (k-NN)  0.288770             NaN    Median Imputation   
 33  k-Nearest Neighbors (k-NN)  0.101504             NaN    Median Imputation   
 34  k-Nearest Neighbors (k-NN)  0.566274             NaN    Median Imputation   
 35  k-Nearest Neighbors (k-NN)  0.131665             NaN    Median Imputation   
 36  k-Nearest Neighbors (k-NN)  0.132548             NaN    Median Imputation   
 37  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 38  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 39  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 40  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 41  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 42  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 43  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 44  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 45  k-Nearest Neighbors (k-NN)  0.750527             NaN    Median Imputation   
 46  k-Nearest Neighbors (k-NN)  0.066897             NaN    Median Imputation   
 47  k-Nearest Neighbors (k-NN)  0.295918             NaN    Median Imputation   
 48  k-Nearest Neighbors (k-NN)  0.109125             NaN    Median Imputation   
 49  k-Nearest Neighbors (k-NN)  0.562155             NaN    Median Imputation   
 50  k-Nearest Neighbors (k-NN)  0.109603             NaN    Median Imputation   
 51  k-Nearest Neighbors (k-NN)  0.124310             NaN    Median Imputation   
 52  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 53  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 54  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 55  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 56  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 57  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 58  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 59  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 60  k-Nearest Neighbors (k-NN)  0.756322             NaN    Median Imputation   
 61  k-Nearest Neighbors (k-NN)  0.080473             NaN    Median Imputation   
 62  k-Nearest Neighbors (k-NN)  0.314815             NaN    Median Imputation   
 63  k-Nearest Neighbors (k-NN)  0.128181             NaN    Median Imputation   
 64  k-Nearest Neighbors (k-NN)  0.574015             NaN    Median Imputation   
 65  k-Nearest Neighbors (k-NN)  0.132169             NaN    Median Imputation   
 66  k-Nearest Neighbors (k-NN)  0.148030             NaN    Median Imputation   
 67  k-Nearest Neighbors (k-NN)       NaN            auto    Median Imputation   
 68  k-Nearest Neighbors (k-NN)       NaN              30    Median Imputation   
 69  k-Nearest Neighbors (k-NN)       NaN       minkowski    Median Imputation   
 70  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 71  k-Nearest Neighbors (k-NN)       NaN            None    Median Imputation   
 72  k-Nearest Neighbors (k-NN)       NaN               5    Median Imputation   
 73  k-Nearest Neighbors (k-NN)       NaN               2    Median Imputation   
 74  k-Nearest Neighbors (k-NN)       NaN         uniform    Median Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 1   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 2   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 3   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 4   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 5   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 6   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 7   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 8   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 9   k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 10  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 11  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 12  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 13  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 14  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 15  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 16  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 17  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 18  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 19  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 20  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 21  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 22  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 23  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 24  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 25  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 26  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 27  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 28  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 29  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 30  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 31  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 32  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 33  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 34  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 35  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 36  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 37  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 38  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 39  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 40  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 41  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 42  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 43  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 44  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 45  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 46  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 47  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 48  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 49  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 50  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 51  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 52  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 53  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 54  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 55  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 56  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 57  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 58  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 59  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 60  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 61  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 62  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 63  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 64  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 65  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 66  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 67  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 68  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 69  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 70  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 71  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 72  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 73  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  
 74  k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.491441             NaN   
 1    Support Vector Machines (SVM)  0.084396             NaN   
 2    Support Vector Machines (SVM)  0.725738             NaN   
 3    Support Vector Machines (SVM)  0.151209             NaN   
 4    Support Vector Machines (SVM)  0.639273             NaN   
 5    Support Vector Machines (SVM)  0.217580             NaN   
 6    Support Vector Machines (SVM)  0.278546             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.480643             NaN   
 23   Support Vector Machines (SVM)  0.061591             NaN   
 24   Support Vector Machines (SVM)  0.774390             NaN   
 25   Support Vector Machines (SVM)  0.114106             NaN   
 26   Support Vector Machines (SVM)  0.681149             NaN   
 27   Support Vector Machines (SVM)  0.266733             NaN   
 28   Support Vector Machines (SVM)  0.362297             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.470898             NaN   
 45   Support Vector Machines (SVM)  0.065777             NaN   
 46   Support Vector Machines (SVM)  0.737968             NaN   
 47   Support Vector Machines (SVM)  0.120788             NaN   
 48   Support Vector Machines (SVM)  0.656951             NaN   
 49   Support Vector Machines (SVM)  0.253474             NaN   
 50   Support Vector Machines (SVM)  0.313902             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.467071             NaN   
 67   Support Vector Machines (SVM)  0.068085             NaN   
 68   Support Vector Machines (SVM)  0.734694             NaN   
 69   Support Vector Machines (SVM)  0.124621             NaN   
 70   Support Vector Machines (SVM)  0.638110             NaN   
 71   Support Vector Machines (SVM)  0.219008             NaN   
 72   Support Vector Machines (SVM)  0.276220             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.494731             NaN   
 89   Support Vector Machines (SVM)  0.081201             NaN   
 90   Support Vector Machines (SVM)  0.763889             NaN   
 91   Support Vector Machines (SVM)  0.146797             NaN   
 92   Support Vector Machines (SVM)  0.670149             NaN   
 93   Support Vector Machines (SVM)  0.271586             NaN   
 94   Support Vector Machines (SVM)  0.340297             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Zero Imputation              Oversampling   
 1        Zero Imputation              Oversampling   
 2        Zero Imputation              Oversampling   
 3        Zero Imputation              Oversampling   
 4        Zero Imputation              Oversampling   
 5        Zero Imputation              Oversampling   
 6        Zero Imputation              Oversampling   
 7        Zero Imputation              Oversampling   
 8        Zero Imputation              Oversampling   
 9        Zero Imputation              Oversampling   
 10       Zero Imputation              Oversampling   
 11       Zero Imputation              Oversampling   
 12       Zero Imputation              Oversampling   
 13       Zero Imputation              Oversampling   
 14       Zero Imputation              Oversampling   
 15       Zero Imputation              Oversampling   
 16       Zero Imputation              Oversampling   
 17       Zero Imputation              Oversampling   
 18       Zero Imputation              Oversampling   
 19       Zero Imputation              Oversampling   
 20       Zero Imputation              Oversampling   
 21       Zero Imputation              Oversampling   
 22       Zero Imputation              Oversampling   
 23       Zero Imputation              Oversampling   
 24       Zero Imputation              Oversampling   
 25       Zero Imputation              Oversampling   
 26       Zero Imputation              Oversampling   
 27       Zero Imputation              Oversampling   
 28       Zero Imputation              Oversampling   
 29       Zero Imputation              Oversampling   
 30       Zero Imputation              Oversampling   
 31       Zero Imputation              Oversampling   
 32       Zero Imputation              Oversampling   
 33       Zero Imputation              Oversampling   
 34       Zero Imputation              Oversampling   
 35       Zero Imputation              Oversampling   
 36       Zero Imputation              Oversampling   
 37       Zero Imputation              Oversampling   
 38       Zero Imputation              Oversampling   
 39       Zero Imputation              Oversampling   
 40       Zero Imputation              Oversampling   
 41       Zero Imputation              Oversampling   
 42       Zero Imputation              Oversampling   
 43       Zero Imputation              Oversampling   
 44       Zero Imputation              Oversampling   
 45       Zero Imputation              Oversampling   
 46       Zero Imputation              Oversampling   
 47       Zero Imputation              Oversampling   
 48       Zero Imputation              Oversampling   
 49       Zero Imputation              Oversampling   
 50       Zero Imputation              Oversampling   
 51       Zero Imputation              Oversampling   
 52       Zero Imputation              Oversampling   
 53       Zero Imputation              Oversampling   
 54       Zero Imputation              Oversampling   
 55       Zero Imputation              Oversampling   
 56       Zero Imputation              Oversampling   
 57       Zero Imputation              Oversampling   
 58       Zero Imputation              Oversampling   
 59       Zero Imputation              Oversampling   
 60       Zero Imputation              Oversampling   
 61       Zero Imputation              Oversampling   
 62       Zero Imputation              Oversampling   
 63       Zero Imputation              Oversampling   
 64       Zero Imputation              Oversampling   
 65       Zero Imputation              Oversampling   
 66       Zero Imputation              Oversampling   
 67       Zero Imputation              Oversampling   
 68       Zero Imputation              Oversampling   
 69       Zero Imputation              Oversampling   
 70       Zero Imputation              Oversampling   
 71       Zero Imputation              Oversampling   
 72       Zero Imputation              Oversampling   
 73       Zero Imputation              Oversampling   
 74       Zero Imputation              Oversampling   
 75       Zero Imputation              Oversampling   
 76       Zero Imputation              Oversampling   
 77       Zero Imputation              Oversampling   
 78       Zero Imputation              Oversampling   
 79       Zero Imputation              Oversampling   
 80       Zero Imputation              Oversampling   
 81       Zero Imputation              Oversampling   
 82       Zero Imputation              Oversampling   
 83       Zero Imputation              Oversampling   
 84       Zero Imputation              Oversampling   
 85       Zero Imputation              Oversampling   
 86       Zero Imputation              Oversampling   
 87       Zero Imputation              Oversampling   
 88       Zero Imputation              Oversampling   
 89       Zero Imputation              Oversampling   
 90       Zero Imputation              Oversampling   
 91       Zero Imputation              Oversampling   
 92       Zero Imputation              Oversampling   
 93       Zero Imputation              Oversampling   
 94       Zero Imputation              Oversampling   
 95       Zero Imputation              Oversampling   
 96       Zero Imputation              Oversampling   
 97       Zero Imputation              Oversampling   
 98       Zero Imputation              Oversampling   
 99       Zero Imputation              Oversampling   
 100      Zero Imputation              Oversampling   
 101      Zero Imputation              Oversampling   
 102      Zero Imputation              Oversampling   
 103      Zero Imputation              Oversampling   
 104      Zero Imputation              Oversampling   
 105      Zero Imputation              Oversampling   
 106      Zero Imputation              Oversampling   
 107      Zero Imputation              Oversampling   
 108      Zero Imputation              Oversampling   
 109      Zero Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Oversampling_Zer...  
 1    Support Vector Machines (SVM)_Oversampling_Zer...  
 2    Support Vector Machines (SVM)_Oversampling_Zer...  
 3    Support Vector Machines (SVM)_Oversampling_Zer...  
 4    Support Vector Machines (SVM)_Oversampling_Zer...  
 5    Support Vector Machines (SVM)_Oversampling_Zer...  
 6    Support Vector Machines (SVM)_Oversampling_Zer...  
 7    Support Vector Machines (SVM)_Oversampling_Zer...  
 8    Support Vector Machines (SVM)_Oversampling_Zer...  
 9    Support Vector Machines (SVM)_Oversampling_Zer...  
 10   Support Vector Machines (SVM)_Oversampling_Zer...  
 11   Support Vector Machines (SVM)_Oversampling_Zer...  
 12   Support Vector Machines (SVM)_Oversampling_Zer...  
 13   Support Vector Machines (SVM)_Oversampling_Zer...  
 14   Support Vector Machines (SVM)_Oversampling_Zer...  
 15   Support Vector Machines (SVM)_Oversampling_Zer...  
 16   Support Vector Machines (SVM)_Oversampling_Zer...  
 17   Support Vector Machines (SVM)_Oversampling_Zer...  
 18   Support Vector Machines (SVM)_Oversampling_Zer...  
 19   Support Vector Machines (SVM)_Oversampling_Zer...  
 20   Support Vector Machines (SVM)_Oversampling_Zer...  
 21   Support Vector Machines (SVM)_Oversampling_Zer...  
 22   Support Vector Machines (SVM)_Oversampling_Zer...  
 23   Support Vector Machines (SVM)_Oversampling_Zer...  
 24   Support Vector Machines (SVM)_Oversampling_Zer...  
 25   Support Vector Machines (SVM)_Oversampling_Zer...  
 26   Support Vector Machines (SVM)_Oversampling_Zer...  
 27   Support Vector Machines (SVM)_Oversampling_Zer...  
 28   Support Vector Machines (SVM)_Oversampling_Zer...  
 29   Support Vector Machines (SVM)_Oversampling_Zer...  
 30   Support Vector Machines (SVM)_Oversampling_Zer...  
 31   Support Vector Machines (SVM)_Oversampling_Zer...  
 32   Support Vector Machines (SVM)_Oversampling_Zer...  
 33   Support Vector Machines (SVM)_Oversampling_Zer...  
 34   Support Vector Machines (SVM)_Oversampling_Zer...  
 35   Support Vector Machines (SVM)_Oversampling_Zer...  
 36   Support Vector Machines (SVM)_Oversampling_Zer...  
 37   Support Vector Machines (SVM)_Oversampling_Zer...  
 38   Support Vector Machines (SVM)_Oversampling_Zer...  
 39   Support Vector Machines (SVM)_Oversampling_Zer...  
 40   Support Vector Machines (SVM)_Oversampling_Zer...  
 41   Support Vector Machines (SVM)_Oversampling_Zer...  
 42   Support Vector Machines (SVM)_Oversampling_Zer...  
 43   Support Vector Machines (SVM)_Oversampling_Zer...  
 44   Support Vector Machines (SVM)_Oversampling_Zer...  
 45   Support Vector Machines (SVM)_Oversampling_Zer...  
 46   Support Vector Machines (SVM)_Oversampling_Zer...  
 47   Support Vector Machines (SVM)_Oversampling_Zer...  
 48   Support Vector Machines (SVM)_Oversampling_Zer...  
 49   Support Vector Machines (SVM)_Oversampling_Zer...  
 50   Support Vector Machines (SVM)_Oversampling_Zer...  
 51   Support Vector Machines (SVM)_Oversampling_Zer...  
 52   Support Vector Machines (SVM)_Oversampling_Zer...  
 53   Support Vector Machines (SVM)_Oversampling_Zer...  
 54   Support Vector Machines (SVM)_Oversampling_Zer...  
 55   Support Vector Machines (SVM)_Oversampling_Zer...  
 56   Support Vector Machines (SVM)_Oversampling_Zer...  
 57   Support Vector Machines (SVM)_Oversampling_Zer...  
 58   Support Vector Machines (SVM)_Oversampling_Zer...  
 59   Support Vector Machines (SVM)_Oversampling_Zer...  
 60   Support Vector Machines (SVM)_Oversampling_Zer...  
 61   Support Vector Machines (SVM)_Oversampling_Zer...  
 62   Support Vector Machines (SVM)_Oversampling_Zer...  
 63   Support Vector Machines (SVM)_Oversampling_Zer...  
 64   Support Vector Machines (SVM)_Oversampling_Zer...  
 65   Support Vector Machines (SVM)_Oversampling_Zer...  
 66   Support Vector Machines (SVM)_Oversampling_Zer...  
 67   Support Vector Machines (SVM)_Oversampling_Zer...  
 68   Support Vector Machines (SVM)_Oversampling_Zer...  
 69   Support Vector Machines (SVM)_Oversampling_Zer...  
 70   Support Vector Machines (SVM)_Oversampling_Zer...  
 71   Support Vector Machines (SVM)_Oversampling_Zer...  
 72   Support Vector Machines (SVM)_Oversampling_Zer...  
 73   Support Vector Machines (SVM)_Oversampling_Zer...  
 74   Support Vector Machines (SVM)_Oversampling_Zer...  
 75   Support Vector Machines (SVM)_Oversampling_Zer...  
 76   Support Vector Machines (SVM)_Oversampling_Zer...  
 77   Support Vector Machines (SVM)_Oversampling_Zer...  
 78   Support Vector Machines (SVM)_Oversampling_Zer...  
 79   Support Vector Machines (SVM)_Oversampling_Zer...  
 80   Support Vector Machines (SVM)_Oversampling_Zer...  
 81   Support Vector Machines (SVM)_Oversampling_Zer...  
 82   Support Vector Machines (SVM)_Oversampling_Zer...  
 83   Support Vector Machines (SVM)_Oversampling_Zer...  
 84   Support Vector Machines (SVM)_Oversampling_Zer...  
 85   Support Vector Machines (SVM)_Oversampling_Zer...  
 86   Support Vector Machines (SVM)_Oversampling_Zer...  
 87   Support Vector Machines (SVM)_Oversampling_Zer...  
 88   Support Vector Machines (SVM)_Oversampling_Zer...  
 89   Support Vector Machines (SVM)_Oversampling_Zer...  
 90   Support Vector Machines (SVM)_Oversampling_Zer...  
 91   Support Vector Machines (SVM)_Oversampling_Zer...  
 92   Support Vector Machines (SVM)_Oversampling_Zer...  
 93   Support Vector Machines (SVM)_Oversampling_Zer...  
 94   Support Vector Machines (SVM)_Oversampling_Zer...  
 95   Support Vector Machines (SVM)_Oversampling_Zer...  
 96   Support Vector Machines (SVM)_Oversampling_Zer...  
 97   Support Vector Machines (SVM)_Oversampling_Zer...  
 98   Support Vector Machines (SVM)_Oversampling_Zer...  
 99   Support Vector Machines (SVM)_Oversampling_Zer...  
 100  Support Vector Machines (SVM)_Oversampling_Zer...  
 101  Support Vector Machines (SVM)_Oversampling_Zer...  
 102  Support Vector Machines (SVM)_Oversampling_Zer...  
 103  Support Vector Machines (SVM)_Oversampling_Zer...  
 104  Support Vector Machines (SVM)_Oversampling_Zer...  
 105  Support Vector Machines (SVM)_Oversampling_Zer...  
 106  Support Vector Machines (SVM)_Oversampling_Zer...  
 107  Support Vector Machines (SVM)_Oversampling_Zer...  
 108  Support Vector Machines (SVM)_Oversampling_Zer...  
 109  Support Vector Machines (SVM)_Oversampling_Zer...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.454569             NaN   
 1    Support Vector Machines (SVM)  0.073091             NaN   
 2    Support Vector Machines (SVM)  0.662447             NaN   
 3    Support Vector Machines (SVM)  0.131656             NaN   
 4    Support Vector Machines (SVM)  0.588358             NaN   
 5    Support Vector Machines (SVM)  0.156935             NaN   
 6    Support Vector Machines (SVM)  0.176716             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.331841             NaN   
 23   Support Vector Machines (SVM)  0.053107             NaN   
 24   Support Vector Machines (SVM)  0.859756             NaN   
 25   Support Vector Machines (SVM)  0.100035             NaN   
 26   Support Vector Machines (SVM)  0.629368             NaN   
 27   Support Vector Machines (SVM)  0.243718             NaN   
 28   Support Vector Machines (SVM)  0.258736             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.343166             NaN   
 45   Support Vector Machines (SVM)  0.058891             NaN   
 46   Support Vector Machines (SVM)  0.823529             NaN   
 47   Support Vector Machines (SVM)  0.109921             NaN   
 48   Support Vector Machines (SVM)  0.620919             NaN   
 49   Support Vector Machines (SVM)  0.209755             NaN   
 50   Support Vector Machines (SVM)  0.241839             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.272919             NaN   
 67   Support Vector Machines (SVM)  0.060658             NaN   
 68   Support Vector Machines (SVM)  0.903061             NaN   
 69   Support Vector Machines (SVM)  0.113680             NaN   
 70   Support Vector Machines (SVM)  0.628517             NaN   
 71   Support Vector Machines (SVM)  0.232415             NaN   
 72   Support Vector Machines (SVM)  0.257034             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.413593             NaN   
 89   Support Vector Machines (SVM)  0.070880             NaN   
 90   Support Vector Machines (SVM)  0.768519             NaN   
 91   Support Vector Machines (SVM)  0.129789             NaN   
 92   Support Vector Machines (SVM)  0.633570             NaN   
 93   Support Vector Machines (SVM)  0.212239             NaN   
 94   Support Vector Machines (SVM)  0.267140             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Mean Imputation              Oversampling   
 1        Mean Imputation              Oversampling   
 2        Mean Imputation              Oversampling   
 3        Mean Imputation              Oversampling   
 4        Mean Imputation              Oversampling   
 5        Mean Imputation              Oversampling   
 6        Mean Imputation              Oversampling   
 7        Mean Imputation              Oversampling   
 8        Mean Imputation              Oversampling   
 9        Mean Imputation              Oversampling   
 10       Mean Imputation              Oversampling   
 11       Mean Imputation              Oversampling   
 12       Mean Imputation              Oversampling   
 13       Mean Imputation              Oversampling   
 14       Mean Imputation              Oversampling   
 15       Mean Imputation              Oversampling   
 16       Mean Imputation              Oversampling   
 17       Mean Imputation              Oversampling   
 18       Mean Imputation              Oversampling   
 19       Mean Imputation              Oversampling   
 20       Mean Imputation              Oversampling   
 21       Mean Imputation              Oversampling   
 22       Mean Imputation              Oversampling   
 23       Mean Imputation              Oversampling   
 24       Mean Imputation              Oversampling   
 25       Mean Imputation              Oversampling   
 26       Mean Imputation              Oversampling   
 27       Mean Imputation              Oversampling   
 28       Mean Imputation              Oversampling   
 29       Mean Imputation              Oversampling   
 30       Mean Imputation              Oversampling   
 31       Mean Imputation              Oversampling   
 32       Mean Imputation              Oversampling   
 33       Mean Imputation              Oversampling   
 34       Mean Imputation              Oversampling   
 35       Mean Imputation              Oversampling   
 36       Mean Imputation              Oversampling   
 37       Mean Imputation              Oversampling   
 38       Mean Imputation              Oversampling   
 39       Mean Imputation              Oversampling   
 40       Mean Imputation              Oversampling   
 41       Mean Imputation              Oversampling   
 42       Mean Imputation              Oversampling   
 43       Mean Imputation              Oversampling   
 44       Mean Imputation              Oversampling   
 45       Mean Imputation              Oversampling   
 46       Mean Imputation              Oversampling   
 47       Mean Imputation              Oversampling   
 48       Mean Imputation              Oversampling   
 49       Mean Imputation              Oversampling   
 50       Mean Imputation              Oversampling   
 51       Mean Imputation              Oversampling   
 52       Mean Imputation              Oversampling   
 53       Mean Imputation              Oversampling   
 54       Mean Imputation              Oversampling   
 55       Mean Imputation              Oversampling   
 56       Mean Imputation              Oversampling   
 57       Mean Imputation              Oversampling   
 58       Mean Imputation              Oversampling   
 59       Mean Imputation              Oversampling   
 60       Mean Imputation              Oversampling   
 61       Mean Imputation              Oversampling   
 62       Mean Imputation              Oversampling   
 63       Mean Imputation              Oversampling   
 64       Mean Imputation              Oversampling   
 65       Mean Imputation              Oversampling   
 66       Mean Imputation              Oversampling   
 67       Mean Imputation              Oversampling   
 68       Mean Imputation              Oversampling   
 69       Mean Imputation              Oversampling   
 70       Mean Imputation              Oversampling   
 71       Mean Imputation              Oversampling   
 72       Mean Imputation              Oversampling   
 73       Mean Imputation              Oversampling   
 74       Mean Imputation              Oversampling   
 75       Mean Imputation              Oversampling   
 76       Mean Imputation              Oversampling   
 77       Mean Imputation              Oversampling   
 78       Mean Imputation              Oversampling   
 79       Mean Imputation              Oversampling   
 80       Mean Imputation              Oversampling   
 81       Mean Imputation              Oversampling   
 82       Mean Imputation              Oversampling   
 83       Mean Imputation              Oversampling   
 84       Mean Imputation              Oversampling   
 85       Mean Imputation              Oversampling   
 86       Mean Imputation              Oversampling   
 87       Mean Imputation              Oversampling   
 88       Mean Imputation              Oversampling   
 89       Mean Imputation              Oversampling   
 90       Mean Imputation              Oversampling   
 91       Mean Imputation              Oversampling   
 92       Mean Imputation              Oversampling   
 93       Mean Imputation              Oversampling   
 94       Mean Imputation              Oversampling   
 95       Mean Imputation              Oversampling   
 96       Mean Imputation              Oversampling   
 97       Mean Imputation              Oversampling   
 98       Mean Imputation              Oversampling   
 99       Mean Imputation              Oversampling   
 100      Mean Imputation              Oversampling   
 101      Mean Imputation              Oversampling   
 102      Mean Imputation              Oversampling   
 103      Mean Imputation              Oversampling   
 104      Mean Imputation              Oversampling   
 105      Mean Imputation              Oversampling   
 106      Mean Imputation              Oversampling   
 107      Mean Imputation              Oversampling   
 108      Mean Imputation              Oversampling   
 109      Mean Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Oversampling_Mea...  
 1    Support Vector Machines (SVM)_Oversampling_Mea...  
 2    Support Vector Machines (SVM)_Oversampling_Mea...  
 3    Support Vector Machines (SVM)_Oversampling_Mea...  
 4    Support Vector Machines (SVM)_Oversampling_Mea...  
 5    Support Vector Machines (SVM)_Oversampling_Mea...  
 6    Support Vector Machines (SVM)_Oversampling_Mea...  
 7    Support Vector Machines (SVM)_Oversampling_Mea...  
 8    Support Vector Machines (SVM)_Oversampling_Mea...  
 9    Support Vector Machines (SVM)_Oversampling_Mea...  
 10   Support Vector Machines (SVM)_Oversampling_Mea...  
 11   Support Vector Machines (SVM)_Oversampling_Mea...  
 12   Support Vector Machines (SVM)_Oversampling_Mea...  
 13   Support Vector Machines (SVM)_Oversampling_Mea...  
 14   Support Vector Machines (SVM)_Oversampling_Mea...  
 15   Support Vector Machines (SVM)_Oversampling_Mea...  
 16   Support Vector Machines (SVM)_Oversampling_Mea...  
 17   Support Vector Machines (SVM)_Oversampling_Mea...  
 18   Support Vector Machines (SVM)_Oversampling_Mea...  
 19   Support Vector Machines (SVM)_Oversampling_Mea...  
 20   Support Vector Machines (SVM)_Oversampling_Mea...  
 21   Support Vector Machines (SVM)_Oversampling_Mea...  
 22   Support Vector Machines (SVM)_Oversampling_Mea...  
 23   Support Vector Machines (SVM)_Oversampling_Mea...  
 24   Support Vector Machines (SVM)_Oversampling_Mea...  
 25   Support Vector Machines (SVM)_Oversampling_Mea...  
 26   Support Vector Machines (SVM)_Oversampling_Mea...  
 27   Support Vector Machines (SVM)_Oversampling_Mea...  
 28   Support Vector Machines (SVM)_Oversampling_Mea...  
 29   Support Vector Machines (SVM)_Oversampling_Mea...  
 30   Support Vector Machines (SVM)_Oversampling_Mea...  
 31   Support Vector Machines (SVM)_Oversampling_Mea...  
 32   Support Vector Machines (SVM)_Oversampling_Mea...  
 33   Support Vector Machines (SVM)_Oversampling_Mea...  
 34   Support Vector Machines (SVM)_Oversampling_Mea...  
 35   Support Vector Machines (SVM)_Oversampling_Mea...  
 36   Support Vector Machines (SVM)_Oversampling_Mea...  
 37   Support Vector Machines (SVM)_Oversampling_Mea...  
 38   Support Vector Machines (SVM)_Oversampling_Mea...  
 39   Support Vector Machines (SVM)_Oversampling_Mea...  
 40   Support Vector Machines (SVM)_Oversampling_Mea...  
 41   Support Vector Machines (SVM)_Oversampling_Mea...  
 42   Support Vector Machines (SVM)_Oversampling_Mea...  
 43   Support Vector Machines (SVM)_Oversampling_Mea...  
 44   Support Vector Machines (SVM)_Oversampling_Mea...  
 45   Support Vector Machines (SVM)_Oversampling_Mea...  
 46   Support Vector Machines (SVM)_Oversampling_Mea...  
 47   Support Vector Machines (SVM)_Oversampling_Mea...  
 48   Support Vector Machines (SVM)_Oversampling_Mea...  
 49   Support Vector Machines (SVM)_Oversampling_Mea...  
 50   Support Vector Machines (SVM)_Oversampling_Mea...  
 51   Support Vector Machines (SVM)_Oversampling_Mea...  
 52   Support Vector Machines (SVM)_Oversampling_Mea...  
 53   Support Vector Machines (SVM)_Oversampling_Mea...  
 54   Support Vector Machines (SVM)_Oversampling_Mea...  
 55   Support Vector Machines (SVM)_Oversampling_Mea...  
 56   Support Vector Machines (SVM)_Oversampling_Mea...  
 57   Support Vector Machines (SVM)_Oversampling_Mea...  
 58   Support Vector Machines (SVM)_Oversampling_Mea...  
 59   Support Vector Machines (SVM)_Oversampling_Mea...  
 60   Support Vector Machines (SVM)_Oversampling_Mea...  
 61   Support Vector Machines (SVM)_Oversampling_Mea...  
 62   Support Vector Machines (SVM)_Oversampling_Mea...  
 63   Support Vector Machines (SVM)_Oversampling_Mea...  
 64   Support Vector Machines (SVM)_Oversampling_Mea...  
 65   Support Vector Machines (SVM)_Oversampling_Mea...  
 66   Support Vector Machines (SVM)_Oversampling_Mea...  
 67   Support Vector Machines (SVM)_Oversampling_Mea...  
 68   Support Vector Machines (SVM)_Oversampling_Mea...  
 69   Support Vector Machines (SVM)_Oversampling_Mea...  
 70   Support Vector Machines (SVM)_Oversampling_Mea...  
 71   Support Vector Machines (SVM)_Oversampling_Mea...  
 72   Support Vector Machines (SVM)_Oversampling_Mea...  
 73   Support Vector Machines (SVM)_Oversampling_Mea...  
 74   Support Vector Machines (SVM)_Oversampling_Mea...  
 75   Support Vector Machines (SVM)_Oversampling_Mea...  
 76   Support Vector Machines (SVM)_Oversampling_Mea...  
 77   Support Vector Machines (SVM)_Oversampling_Mea...  
 78   Support Vector Machines (SVM)_Oversampling_Mea...  
 79   Support Vector Machines (SVM)_Oversampling_Mea...  
 80   Support Vector Machines (SVM)_Oversampling_Mea...  
 81   Support Vector Machines (SVM)_Oversampling_Mea...  
 82   Support Vector Machines (SVM)_Oversampling_Mea...  
 83   Support Vector Machines (SVM)_Oversampling_Mea...  
 84   Support Vector Machines (SVM)_Oversampling_Mea...  
 85   Support Vector Machines (SVM)_Oversampling_Mea...  
 86   Support Vector Machines (SVM)_Oversampling_Mea...  
 87   Support Vector Machines (SVM)_Oversampling_Mea...  
 88   Support Vector Machines (SVM)_Oversampling_Mea...  
 89   Support Vector Machines (SVM)_Oversampling_Mea...  
 90   Support Vector Machines (SVM)_Oversampling_Mea...  
 91   Support Vector Machines (SVM)_Oversampling_Mea...  
 92   Support Vector Machines (SVM)_Oversampling_Mea...  
 93   Support Vector Machines (SVM)_Oversampling_Mea...  
 94   Support Vector Machines (SVM)_Oversampling_Mea...  
 95   Support Vector Machines (SVM)_Oversampling_Mea...  
 96   Support Vector Machines (SVM)_Oversampling_Mea...  
 97   Support Vector Machines (SVM)_Oversampling_Mea...  
 98   Support Vector Machines (SVM)_Oversampling_Mea...  
 99   Support Vector Machines (SVM)_Oversampling_Mea...  
 100  Support Vector Machines (SVM)_Oversampling_Mea...  
 101  Support Vector Machines (SVM)_Oversampling_Mea...  
 102  Support Vector Machines (SVM)_Oversampling_Mea...  
 103  Support Vector Machines (SVM)_Oversampling_Mea...  
 104  Support Vector Machines (SVM)_Oversampling_Mea...  
 105  Support Vector Machines (SVM)_Oversampling_Mea...  
 106  Support Vector Machines (SVM)_Oversampling_Mea...  
 107  Support Vector Machines (SVM)_Oversampling_Mea...  
 108  Support Vector Machines (SVM)_Oversampling_Mea...  
 109  Support Vector Machines (SVM)_Oversampling_Mea...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.405583             NaN   
 1    Support Vector Machines (SVM)  0.080565             NaN   
 2    Support Vector Machines (SVM)  0.818565             NaN   
 3    Support Vector Machines (SVM)  0.146692             NaN   
 4    Support Vector Machines (SVM)  0.643815             NaN   
 5    Support Vector Machines (SVM)  0.233736             NaN   
 6    Support Vector Machines (SVM)  0.287630             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.383461             NaN   
 23   Support Vector Machines (SVM)  0.055533             NaN   
 24   Support Vector Machines (SVM)  0.829268             NaN   
 25   Support Vector Machines (SVM)  0.104095             NaN   
 26   Support Vector Machines (SVM)  0.660595             NaN   
 27   Support Vector Machines (SVM)  0.288291             NaN   
 28   Support Vector Machines (SVM)  0.321190             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.378720             NaN   
 45   Support Vector Machines (SVM)  0.062450             NaN   
 46   Support Vector Machines (SVM)  0.828877             NaN   
 47   Support Vector Machines (SVM)  0.116148             NaN   
 48   Support Vector Machines (SVM)  0.657651             NaN   
 49   Support Vector Machines (SVM)  0.241381             NaN   
 50   Support Vector Machines (SVM)  0.315302             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.373815             NaN   
 67   Support Vector Machines (SVM)  0.063974             NaN   
 68   Support Vector Machines (SVM)  0.816327             NaN   
 69   Support Vector Machines (SVM)  0.118650             NaN   
 70   Support Vector Machines (SVM)  0.640518             NaN   
 71   Support Vector Machines (SVM)  0.251837             NaN   
 72   Support Vector Machines (SVM)  0.281036             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.400421             NaN   
 89   Support Vector Machines (SVM)  0.074028             NaN   
 90   Support Vector Machines (SVM)  0.828704             NaN   
 91   Support Vector Machines (SVM)  0.135915             NaN   
 92   Support Vector Machines (SVM)  0.670456             NaN   
 93   Support Vector Machines (SVM)  0.274581             NaN   
 94   Support Vector Machines (SVM)  0.340913             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0      Median Imputation              Oversampling   
 1      Median Imputation              Oversampling   
 2      Median Imputation              Oversampling   
 3      Median Imputation              Oversampling   
 4      Median Imputation              Oversampling   
 5      Median Imputation              Oversampling   
 6      Median Imputation              Oversampling   
 7      Median Imputation              Oversampling   
 8      Median Imputation              Oversampling   
 9      Median Imputation              Oversampling   
 10     Median Imputation              Oversampling   
 11     Median Imputation              Oversampling   
 12     Median Imputation              Oversampling   
 13     Median Imputation              Oversampling   
 14     Median Imputation              Oversampling   
 15     Median Imputation              Oversampling   
 16     Median Imputation              Oversampling   
 17     Median Imputation              Oversampling   
 18     Median Imputation              Oversampling   
 19     Median Imputation              Oversampling   
 20     Median Imputation              Oversampling   
 21     Median Imputation              Oversampling   
 22     Median Imputation              Oversampling   
 23     Median Imputation              Oversampling   
 24     Median Imputation              Oversampling   
 25     Median Imputation              Oversampling   
 26     Median Imputation              Oversampling   
 27     Median Imputation              Oversampling   
 28     Median Imputation              Oversampling   
 29     Median Imputation              Oversampling   
 30     Median Imputation              Oversampling   
 31     Median Imputation              Oversampling   
 32     Median Imputation              Oversampling   
 33     Median Imputation              Oversampling   
 34     Median Imputation              Oversampling   
 35     Median Imputation              Oversampling   
 36     Median Imputation              Oversampling   
 37     Median Imputation              Oversampling   
 38     Median Imputation              Oversampling   
 39     Median Imputation              Oversampling   
 40     Median Imputation              Oversampling   
 41     Median Imputation              Oversampling   
 42     Median Imputation              Oversampling   
 43     Median Imputation              Oversampling   
 44     Median Imputation              Oversampling   
 45     Median Imputation              Oversampling   
 46     Median Imputation              Oversampling   
 47     Median Imputation              Oversampling   
 48     Median Imputation              Oversampling   
 49     Median Imputation              Oversampling   
 50     Median Imputation              Oversampling   
 51     Median Imputation              Oversampling   
 52     Median Imputation              Oversampling   
 53     Median Imputation              Oversampling   
 54     Median Imputation              Oversampling   
 55     Median Imputation              Oversampling   
 56     Median Imputation              Oversampling   
 57     Median Imputation              Oversampling   
 58     Median Imputation              Oversampling   
 59     Median Imputation              Oversampling   
 60     Median Imputation              Oversampling   
 61     Median Imputation              Oversampling   
 62     Median Imputation              Oversampling   
 63     Median Imputation              Oversampling   
 64     Median Imputation              Oversampling   
 65     Median Imputation              Oversampling   
 66     Median Imputation              Oversampling   
 67     Median Imputation              Oversampling   
 68     Median Imputation              Oversampling   
 69     Median Imputation              Oversampling   
 70     Median Imputation              Oversampling   
 71     Median Imputation              Oversampling   
 72     Median Imputation              Oversampling   
 73     Median Imputation              Oversampling   
 74     Median Imputation              Oversampling   
 75     Median Imputation              Oversampling   
 76     Median Imputation              Oversampling   
 77     Median Imputation              Oversampling   
 78     Median Imputation              Oversampling   
 79     Median Imputation              Oversampling   
 80     Median Imputation              Oversampling   
 81     Median Imputation              Oversampling   
 82     Median Imputation              Oversampling   
 83     Median Imputation              Oversampling   
 84     Median Imputation              Oversampling   
 85     Median Imputation              Oversampling   
 86     Median Imputation              Oversampling   
 87     Median Imputation              Oversampling   
 88     Median Imputation              Oversampling   
 89     Median Imputation              Oversampling   
 90     Median Imputation              Oversampling   
 91     Median Imputation              Oversampling   
 92     Median Imputation              Oversampling   
 93     Median Imputation              Oversampling   
 94     Median Imputation              Oversampling   
 95     Median Imputation              Oversampling   
 96     Median Imputation              Oversampling   
 97     Median Imputation              Oversampling   
 98     Median Imputation              Oversampling   
 99     Median Imputation              Oversampling   
 100    Median Imputation              Oversampling   
 101    Median Imputation              Oversampling   
 102    Median Imputation              Oversampling   
 103    Median Imputation              Oversampling   
 104    Median Imputation              Oversampling   
 105    Median Imputation              Oversampling   
 106    Median Imputation              Oversampling   
 107    Median Imputation              Oversampling   
 108    Median Imputation              Oversampling   
 109    Median Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Oversampling_Med...  
 1    Support Vector Machines (SVM)_Oversampling_Med...  
 2    Support Vector Machines (SVM)_Oversampling_Med...  
 3    Support Vector Machines (SVM)_Oversampling_Med...  
 4    Support Vector Machines (SVM)_Oversampling_Med...  
 5    Support Vector Machines (SVM)_Oversampling_Med...  
 6    Support Vector Machines (SVM)_Oversampling_Med...  
 7    Support Vector Machines (SVM)_Oversampling_Med...  
 8    Support Vector Machines (SVM)_Oversampling_Med...  
 9    Support Vector Machines (SVM)_Oversampling_Med...  
 10   Support Vector Machines (SVM)_Oversampling_Med...  
 11   Support Vector Machines (SVM)_Oversampling_Med...  
 12   Support Vector Machines (SVM)_Oversampling_Med...  
 13   Support Vector Machines (SVM)_Oversampling_Med...  
 14   Support Vector Machines (SVM)_Oversampling_Med...  
 15   Support Vector Machines (SVM)_Oversampling_Med...  
 16   Support Vector Machines (SVM)_Oversampling_Med...  
 17   Support Vector Machines (SVM)_Oversampling_Med...  
 18   Support Vector Machines (SVM)_Oversampling_Med...  
 19   Support Vector Machines (SVM)_Oversampling_Med...  
 20   Support Vector Machines (SVM)_Oversampling_Med...  
 21   Support Vector Machines (SVM)_Oversampling_Med...  
 22   Support Vector Machines (SVM)_Oversampling_Med...  
 23   Support Vector Machines (SVM)_Oversampling_Med...  
 24   Support Vector Machines (SVM)_Oversampling_Med...  
 25   Support Vector Machines (SVM)_Oversampling_Med...  
 26   Support Vector Machines (SVM)_Oversampling_Med...  
 27   Support Vector Machines (SVM)_Oversampling_Med...  
 28   Support Vector Machines (SVM)_Oversampling_Med...  
 29   Support Vector Machines (SVM)_Oversampling_Med...  
 30   Support Vector Machines (SVM)_Oversampling_Med...  
 31   Support Vector Machines (SVM)_Oversampling_Med...  
 32   Support Vector Machines (SVM)_Oversampling_Med...  
 33   Support Vector Machines (SVM)_Oversampling_Med...  
 34   Support Vector Machines (SVM)_Oversampling_Med...  
 35   Support Vector Machines (SVM)_Oversampling_Med...  
 36   Support Vector Machines (SVM)_Oversampling_Med...  
 37   Support Vector Machines (SVM)_Oversampling_Med...  
 38   Support Vector Machines (SVM)_Oversampling_Med...  
 39   Support Vector Machines (SVM)_Oversampling_Med...  
 40   Support Vector Machines (SVM)_Oversampling_Med...  
 41   Support Vector Machines (SVM)_Oversampling_Med...  
 42   Support Vector Machines (SVM)_Oversampling_Med...  
 43   Support Vector Machines (SVM)_Oversampling_Med...  
 44   Support Vector Machines (SVM)_Oversampling_Med...  
 45   Support Vector Machines (SVM)_Oversampling_Med...  
 46   Support Vector Machines (SVM)_Oversampling_Med...  
 47   Support Vector Machines (SVM)_Oversampling_Med...  
 48   Support Vector Machines (SVM)_Oversampling_Med...  
 49   Support Vector Machines (SVM)_Oversampling_Med...  
 50   Support Vector Machines (SVM)_Oversampling_Med...  
 51   Support Vector Machines (SVM)_Oversampling_Med...  
 52   Support Vector Machines (SVM)_Oversampling_Med...  
 53   Support Vector Machines (SVM)_Oversampling_Med...  
 54   Support Vector Machines (SVM)_Oversampling_Med...  
 55   Support Vector Machines (SVM)_Oversampling_Med...  
 56   Support Vector Machines (SVM)_Oversampling_Med...  
 57   Support Vector Machines (SVM)_Oversampling_Med...  
 58   Support Vector Machines (SVM)_Oversampling_Med...  
 59   Support Vector Machines (SVM)_Oversampling_Med...  
 60   Support Vector Machines (SVM)_Oversampling_Med...  
 61   Support Vector Machines (SVM)_Oversampling_Med...  
 62   Support Vector Machines (SVM)_Oversampling_Med...  
 63   Support Vector Machines (SVM)_Oversampling_Med...  
 64   Support Vector Machines (SVM)_Oversampling_Med...  
 65   Support Vector Machines (SVM)_Oversampling_Med...  
 66   Support Vector Machines (SVM)_Oversampling_Med...  
 67   Support Vector Machines (SVM)_Oversampling_Med...  
 68   Support Vector Machines (SVM)_Oversampling_Med...  
 69   Support Vector Machines (SVM)_Oversampling_Med...  
 70   Support Vector Machines (SVM)_Oversampling_Med...  
 71   Support Vector Machines (SVM)_Oversampling_Med...  
 72   Support Vector Machines (SVM)_Oversampling_Med...  
 73   Support Vector Machines (SVM)_Oversampling_Med...  
 74   Support Vector Machines (SVM)_Oversampling_Med...  
 75   Support Vector Machines (SVM)_Oversampling_Med...  
 76   Support Vector Machines (SVM)_Oversampling_Med...  
 77   Support Vector Machines (SVM)_Oversampling_Med...  
 78   Support Vector Machines (SVM)_Oversampling_Med...  
 79   Support Vector Machines (SVM)_Oversampling_Med...  
 80   Support Vector Machines (SVM)_Oversampling_Med...  
 81   Support Vector Machines (SVM)_Oversampling_Med...  
 82   Support Vector Machines (SVM)_Oversampling_Med...  
 83   Support Vector Machines (SVM)_Oversampling_Med...  
 84   Support Vector Machines (SVM)_Oversampling_Med...  
 85   Support Vector Machines (SVM)_Oversampling_Med...  
 86   Support Vector Machines (SVM)_Oversampling_Med...  
 87   Support Vector Machines (SVM)_Oversampling_Med...  
 88   Support Vector Machines (SVM)_Oversampling_Med...  
 89   Support Vector Machines (SVM)_Oversampling_Med...  
 90   Support Vector Machines (SVM)_Oversampling_Med...  
 91   Support Vector Machines (SVM)_Oversampling_Med...  
 92   Support Vector Machines (SVM)_Oversampling_Med...  
 93   Support Vector Machines (SVM)_Oversampling_Med...  
 94   Support Vector Machines (SVM)_Oversampling_Med...  
 95   Support Vector Machines (SVM)_Oversampling_Med...  
 96   Support Vector Machines (SVM)_Oversampling_Med...  
 97   Support Vector Machines (SVM)_Oversampling_Med...  
 98   Support Vector Machines (SVM)_Oversampling_Med...  
 99   Support Vector Machines (SVM)_Oversampling_Med...  
 100  Support Vector Machines (SVM)_Oversampling_Med...  
 101  Support Vector Machines (SVM)_Oversampling_Med...  
 102  Support Vector Machines (SVM)_Oversampling_Med...  
 103  Support Vector Machines (SVM)_Oversampling_Med...  
 104  Support Vector Machines (SVM)_Oversampling_Med...  
 105  Support Vector Machines (SVM)_Oversampling_Med...  
 106  Support Vector Machines (SVM)_Oversampling_Med...  
 107  Support Vector Machines (SVM)_Oversampling_Med...  
 108  Support Vector Machines (SVM)_Oversampling_Med...  
 109  Support Vector Machines (SVM)_Oversampling_Med...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.397682             NaN   
 1    Support Vector Machines (SVM)  0.077494             NaN   
 2    Support Vector Machines (SVM)  0.793249             NaN   
 3    Support Vector Machines (SVM)  0.141194             NaN   
 4    Support Vector Machines (SVM)  0.627430             NaN   
 5    Support Vector Machines (SVM)  0.200847             NaN   
 6    Support Vector Machines (SVM)  0.254861             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.419805             NaN   
 23   Support Vector Machines (SVM)  0.058084             NaN   
 24   Support Vector Machines (SVM)  0.817073             NaN   
 25   Support Vector Machines (SVM)  0.108458             NaN   
 26   Support Vector Machines (SVM)  0.671298             NaN   
 27   Support Vector Machines (SVM)  0.274677             NaN   
 28   Support Vector Machines (SVM)  0.342596             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.400053             NaN   
 45   Support Vector Machines (SVM)  0.060899             NaN   
 46   Support Vector Machines (SVM)  0.775401             NaN   
 47   Support Vector Machines (SVM)  0.112928             NaN   
 48   Support Vector Machines (SVM)  0.642343             NaN   
 49   Support Vector Machines (SVM)  0.226702             NaN   
 50   Support Vector Machines (SVM)  0.284686             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.413593             NaN   
 67   Support Vector Machines (SVM)  0.066610             NaN   
 68   Support Vector Machines (SVM)  0.795918             NaN   
 69   Support Vector Machines (SVM)  0.122931             NaN   
 70   Support Vector Machines (SVM)  0.639204             NaN   
 71   Support Vector Machines (SVM)  0.207200             NaN   
 72   Support Vector Machines (SVM)  0.278408             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.453635             NaN   
 89   Support Vector Machines (SVM)  0.073462             NaN   
 90   Support Vector Machines (SVM)  0.740741             NaN   
 91   Support Vector Machines (SVM)  0.133668             NaN   
 92   Support Vector Machines (SVM)  0.657546             NaN   
 93   Support Vector Machines (SVM)  0.258975             NaN   
 94   Support Vector Machines (SVM)  0.315092             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Zero Imputation             Undersampling   
 1        Zero Imputation             Undersampling   
 2        Zero Imputation             Undersampling   
 3        Zero Imputation             Undersampling   
 4        Zero Imputation             Undersampling   
 5        Zero Imputation             Undersampling   
 6        Zero Imputation             Undersampling   
 7        Zero Imputation             Undersampling   
 8        Zero Imputation             Undersampling   
 9        Zero Imputation             Undersampling   
 10       Zero Imputation             Undersampling   
 11       Zero Imputation             Undersampling   
 12       Zero Imputation             Undersampling   
 13       Zero Imputation             Undersampling   
 14       Zero Imputation             Undersampling   
 15       Zero Imputation             Undersampling   
 16       Zero Imputation             Undersampling   
 17       Zero Imputation             Undersampling   
 18       Zero Imputation             Undersampling   
 19       Zero Imputation             Undersampling   
 20       Zero Imputation             Undersampling   
 21       Zero Imputation             Undersampling   
 22       Zero Imputation             Undersampling   
 23       Zero Imputation             Undersampling   
 24       Zero Imputation             Undersampling   
 25       Zero Imputation             Undersampling   
 26       Zero Imputation             Undersampling   
 27       Zero Imputation             Undersampling   
 28       Zero Imputation             Undersampling   
 29       Zero Imputation             Undersampling   
 30       Zero Imputation             Undersampling   
 31       Zero Imputation             Undersampling   
 32       Zero Imputation             Undersampling   
 33       Zero Imputation             Undersampling   
 34       Zero Imputation             Undersampling   
 35       Zero Imputation             Undersampling   
 36       Zero Imputation             Undersampling   
 37       Zero Imputation             Undersampling   
 38       Zero Imputation             Undersampling   
 39       Zero Imputation             Undersampling   
 40       Zero Imputation             Undersampling   
 41       Zero Imputation             Undersampling   
 42       Zero Imputation             Undersampling   
 43       Zero Imputation             Undersampling   
 44       Zero Imputation             Undersampling   
 45       Zero Imputation             Undersampling   
 46       Zero Imputation             Undersampling   
 47       Zero Imputation             Undersampling   
 48       Zero Imputation             Undersampling   
 49       Zero Imputation             Undersampling   
 50       Zero Imputation             Undersampling   
 51       Zero Imputation             Undersampling   
 52       Zero Imputation             Undersampling   
 53       Zero Imputation             Undersampling   
 54       Zero Imputation             Undersampling   
 55       Zero Imputation             Undersampling   
 56       Zero Imputation             Undersampling   
 57       Zero Imputation             Undersampling   
 58       Zero Imputation             Undersampling   
 59       Zero Imputation             Undersampling   
 60       Zero Imputation             Undersampling   
 61       Zero Imputation             Undersampling   
 62       Zero Imputation             Undersampling   
 63       Zero Imputation             Undersampling   
 64       Zero Imputation             Undersampling   
 65       Zero Imputation             Undersampling   
 66       Zero Imputation             Undersampling   
 67       Zero Imputation             Undersampling   
 68       Zero Imputation             Undersampling   
 69       Zero Imputation             Undersampling   
 70       Zero Imputation             Undersampling   
 71       Zero Imputation             Undersampling   
 72       Zero Imputation             Undersampling   
 73       Zero Imputation             Undersampling   
 74       Zero Imputation             Undersampling   
 75       Zero Imputation             Undersampling   
 76       Zero Imputation             Undersampling   
 77       Zero Imputation             Undersampling   
 78       Zero Imputation             Undersampling   
 79       Zero Imputation             Undersampling   
 80       Zero Imputation             Undersampling   
 81       Zero Imputation             Undersampling   
 82       Zero Imputation             Undersampling   
 83       Zero Imputation             Undersampling   
 84       Zero Imputation             Undersampling   
 85       Zero Imputation             Undersampling   
 86       Zero Imputation             Undersampling   
 87       Zero Imputation             Undersampling   
 88       Zero Imputation             Undersampling   
 89       Zero Imputation             Undersampling   
 90       Zero Imputation             Undersampling   
 91       Zero Imputation             Undersampling   
 92       Zero Imputation             Undersampling   
 93       Zero Imputation             Undersampling   
 94       Zero Imputation             Undersampling   
 95       Zero Imputation             Undersampling   
 96       Zero Imputation             Undersampling   
 97       Zero Imputation             Undersampling   
 98       Zero Imputation             Undersampling   
 99       Zero Imputation             Undersampling   
 100      Zero Imputation             Undersampling   
 101      Zero Imputation             Undersampling   
 102      Zero Imputation             Undersampling   
 103      Zero Imputation             Undersampling   
 104      Zero Imputation             Undersampling   
 105      Zero Imputation             Undersampling   
 106      Zero Imputation             Undersampling   
 107      Zero Imputation             Undersampling   
 108      Zero Imputation             Undersampling   
 109      Zero Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Undersampling_Ze...  
 1    Support Vector Machines (SVM)_Undersampling_Ze...  
 2    Support Vector Machines (SVM)_Undersampling_Ze...  
 3    Support Vector Machines (SVM)_Undersampling_Ze...  
 4    Support Vector Machines (SVM)_Undersampling_Ze...  
 5    Support Vector Machines (SVM)_Undersampling_Ze...  
 6    Support Vector Machines (SVM)_Undersampling_Ze...  
 7    Support Vector Machines (SVM)_Undersampling_Ze...  
 8    Support Vector Machines (SVM)_Undersampling_Ze...  
 9    Support Vector Machines (SVM)_Undersampling_Ze...  
 10   Support Vector Machines (SVM)_Undersampling_Ze...  
 11   Support Vector Machines (SVM)_Undersampling_Ze...  
 12   Support Vector Machines (SVM)_Undersampling_Ze...  
 13   Support Vector Machines (SVM)_Undersampling_Ze...  
 14   Support Vector Machines (SVM)_Undersampling_Ze...  
 15   Support Vector Machines (SVM)_Undersampling_Ze...  
 16   Support Vector Machines (SVM)_Undersampling_Ze...  
 17   Support Vector Machines (SVM)_Undersampling_Ze...  
 18   Support Vector Machines (SVM)_Undersampling_Ze...  
 19   Support Vector Machines (SVM)_Undersampling_Ze...  
 20   Support Vector Machines (SVM)_Undersampling_Ze...  
 21   Support Vector Machines (SVM)_Undersampling_Ze...  
 22   Support Vector Machines (SVM)_Undersampling_Ze...  
 23   Support Vector Machines (SVM)_Undersampling_Ze...  
 24   Support Vector Machines (SVM)_Undersampling_Ze...  
 25   Support Vector Machines (SVM)_Undersampling_Ze...  
 26   Support Vector Machines (SVM)_Undersampling_Ze...  
 27   Support Vector Machines (SVM)_Undersampling_Ze...  
 28   Support Vector Machines (SVM)_Undersampling_Ze...  
 29   Support Vector Machines (SVM)_Undersampling_Ze...  
 30   Support Vector Machines (SVM)_Undersampling_Ze...  
 31   Support Vector Machines (SVM)_Undersampling_Ze...  
 32   Support Vector Machines (SVM)_Undersampling_Ze...  
 33   Support Vector Machines (SVM)_Undersampling_Ze...  
 34   Support Vector Machines (SVM)_Undersampling_Ze...  
 35   Support Vector Machines (SVM)_Undersampling_Ze...  
 36   Support Vector Machines (SVM)_Undersampling_Ze...  
 37   Support Vector Machines (SVM)_Undersampling_Ze...  
 38   Support Vector Machines (SVM)_Undersampling_Ze...  
 39   Support Vector Machines (SVM)_Undersampling_Ze...  
 40   Support Vector Machines (SVM)_Undersampling_Ze...  
 41   Support Vector Machines (SVM)_Undersampling_Ze...  
 42   Support Vector Machines (SVM)_Undersampling_Ze...  
 43   Support Vector Machines (SVM)_Undersampling_Ze...  
 44   Support Vector Machines (SVM)_Undersampling_Ze...  
 45   Support Vector Machines (SVM)_Undersampling_Ze...  
 46   Support Vector Machines (SVM)_Undersampling_Ze...  
 47   Support Vector Machines (SVM)_Undersampling_Ze...  
 48   Support Vector Machines (SVM)_Undersampling_Ze...  
 49   Support Vector Machines (SVM)_Undersampling_Ze...  
 50   Support Vector Machines (SVM)_Undersampling_Ze...  
 51   Support Vector Machines (SVM)_Undersampling_Ze...  
 52   Support Vector Machines (SVM)_Undersampling_Ze...  
 53   Support Vector Machines (SVM)_Undersampling_Ze...  
 54   Support Vector Machines (SVM)_Undersampling_Ze...  
 55   Support Vector Machines (SVM)_Undersampling_Ze...  
 56   Support Vector Machines (SVM)_Undersampling_Ze...  
 57   Support Vector Machines (SVM)_Undersampling_Ze...  
 58   Support Vector Machines (SVM)_Undersampling_Ze...  
 59   Support Vector Machines (SVM)_Undersampling_Ze...  
 60   Support Vector Machines (SVM)_Undersampling_Ze...  
 61   Support Vector Machines (SVM)_Undersampling_Ze...  
 62   Support Vector Machines (SVM)_Undersampling_Ze...  
 63   Support Vector Machines (SVM)_Undersampling_Ze...  
 64   Support Vector Machines (SVM)_Undersampling_Ze...  
 65   Support Vector Machines (SVM)_Undersampling_Ze...  
 66   Support Vector Machines (SVM)_Undersampling_Ze...  
 67   Support Vector Machines (SVM)_Undersampling_Ze...  
 68   Support Vector Machines (SVM)_Undersampling_Ze...  
 69   Support Vector Machines (SVM)_Undersampling_Ze...  
 70   Support Vector Machines (SVM)_Undersampling_Ze...  
 71   Support Vector Machines (SVM)_Undersampling_Ze...  
 72   Support Vector Machines (SVM)_Undersampling_Ze...  
 73   Support Vector Machines (SVM)_Undersampling_Ze...  
 74   Support Vector Machines (SVM)_Undersampling_Ze...  
 75   Support Vector Machines (SVM)_Undersampling_Ze...  
 76   Support Vector Machines (SVM)_Undersampling_Ze...  
 77   Support Vector Machines (SVM)_Undersampling_Ze...  
 78   Support Vector Machines (SVM)_Undersampling_Ze...  
 79   Support Vector Machines (SVM)_Undersampling_Ze...  
 80   Support Vector Machines (SVM)_Undersampling_Ze...  
 81   Support Vector Machines (SVM)_Undersampling_Ze...  
 82   Support Vector Machines (SVM)_Undersampling_Ze...  
 83   Support Vector Machines (SVM)_Undersampling_Ze...  
 84   Support Vector Machines (SVM)_Undersampling_Ze...  
 85   Support Vector Machines (SVM)_Undersampling_Ze...  
 86   Support Vector Machines (SVM)_Undersampling_Ze...  
 87   Support Vector Machines (SVM)_Undersampling_Ze...  
 88   Support Vector Machines (SVM)_Undersampling_Ze...  
 89   Support Vector Machines (SVM)_Undersampling_Ze...  
 90   Support Vector Machines (SVM)_Undersampling_Ze...  
 91   Support Vector Machines (SVM)_Undersampling_Ze...  
 92   Support Vector Machines (SVM)_Undersampling_Ze...  
 93   Support Vector Machines (SVM)_Undersampling_Ze...  
 94   Support Vector Machines (SVM)_Undersampling_Ze...  
 95   Support Vector Machines (SVM)_Undersampling_Ze...  
 96   Support Vector Machines (SVM)_Undersampling_Ze...  
 97   Support Vector Machines (SVM)_Undersampling_Ze...  
 98   Support Vector Machines (SVM)_Undersampling_Ze...  
 99   Support Vector Machines (SVM)_Undersampling_Ze...  
 100  Support Vector Machines (SVM)_Undersampling_Ze...  
 101  Support Vector Machines (SVM)_Undersampling_Ze...  
 102  Support Vector Machines (SVM)_Undersampling_Ze...  
 103  Support Vector Machines (SVM)_Undersampling_Ze...  
 104  Support Vector Machines (SVM)_Undersampling_Ze...  
 105  Support Vector Machines (SVM)_Undersampling_Ze...  
 106  Support Vector Machines (SVM)_Undersampling_Ze...  
 107  Support Vector Machines (SVM)_Undersampling_Ze...  
 108  Support Vector Machines (SVM)_Undersampling_Ze...  
 109  Support Vector Machines (SVM)_Undersampling_Ze...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.423492             NaN   
 1    Support Vector Machines (SVM)  0.065450             NaN   
 2    Support Vector Machines (SVM)  0.620253             NaN   
 3    Support Vector Machines (SVM)  0.118405             NaN   
 4    Support Vector Machines (SVM)  0.553104             NaN   
 5    Support Vector Machines (SVM)  0.126036             NaN   
 6    Support Vector Machines (SVM)  0.106207             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.160126             NaN   
 23   Support Vector Machines (SVM)  0.047833             NaN   
 24   Support Vector Machines (SVM)  0.975610             NaN   
 25   Support Vector Machines (SVM)  0.091194             NaN   
 26   Support Vector Machines (SVM)  0.632623             NaN   
 27   Support Vector Machines (SVM)  0.242155             NaN   
 28   Support Vector Machines (SVM)  0.265246             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.171188             NaN   
 45   Support Vector Machines (SVM)  0.053410             NaN   
 46   Support Vector Machines (SVM)  0.946524             NaN   
 47   Support Vector Machines (SVM)  0.101114             NaN   
 48   Support Vector Machines (SVM)  0.577636             NaN   
 49   Support Vector Machines (SVM)  0.161832             NaN   
 50   Support Vector Machines (SVM)  0.155273             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.207850             NaN   
 67   Support Vector Machines (SVM)  0.057880             NaN   
 68   Support Vector Machines (SVM)  0.938776             NaN   
 69   Support Vector Machines (SVM)  0.109037             NaN   
 70   Support Vector Machines (SVM)  0.563563             NaN   
 71   Support Vector Machines (SVM)  0.159700             NaN   
 72   Support Vector Machines (SVM)  0.127126             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.181243             NaN   
 89   Support Vector Machines (SVM)  0.062878             NaN   
 90   Support Vector Machines (SVM)  0.962963             NaN   
 91   Support Vector Machines (SVM)  0.118048             NaN   
 92   Support Vector Machines (SVM)  0.580049             NaN   
 93   Support Vector Machines (SVM)  0.191511             NaN   
 94   Support Vector Machines (SVM)  0.160097             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Mean Imputation             Undersampling   
 1        Mean Imputation             Undersampling   
 2        Mean Imputation             Undersampling   
 3        Mean Imputation             Undersampling   
 4        Mean Imputation             Undersampling   
 5        Mean Imputation             Undersampling   
 6        Mean Imputation             Undersampling   
 7        Mean Imputation             Undersampling   
 8        Mean Imputation             Undersampling   
 9        Mean Imputation             Undersampling   
 10       Mean Imputation             Undersampling   
 11       Mean Imputation             Undersampling   
 12       Mean Imputation             Undersampling   
 13       Mean Imputation             Undersampling   
 14       Mean Imputation             Undersampling   
 15       Mean Imputation             Undersampling   
 16       Mean Imputation             Undersampling   
 17       Mean Imputation             Undersampling   
 18       Mean Imputation             Undersampling   
 19       Mean Imputation             Undersampling   
 20       Mean Imputation             Undersampling   
 21       Mean Imputation             Undersampling   
 22       Mean Imputation             Undersampling   
 23       Mean Imputation             Undersampling   
 24       Mean Imputation             Undersampling   
 25       Mean Imputation             Undersampling   
 26       Mean Imputation             Undersampling   
 27       Mean Imputation             Undersampling   
 28       Mean Imputation             Undersampling   
 29       Mean Imputation             Undersampling   
 30       Mean Imputation             Undersampling   
 31       Mean Imputation             Undersampling   
 32       Mean Imputation             Undersampling   
 33       Mean Imputation             Undersampling   
 34       Mean Imputation             Undersampling   
 35       Mean Imputation             Undersampling   
 36       Mean Imputation             Undersampling   
 37       Mean Imputation             Undersampling   
 38       Mean Imputation             Undersampling   
 39       Mean Imputation             Undersampling   
 40       Mean Imputation             Undersampling   
 41       Mean Imputation             Undersampling   
 42       Mean Imputation             Undersampling   
 43       Mean Imputation             Undersampling   
 44       Mean Imputation             Undersampling   
 45       Mean Imputation             Undersampling   
 46       Mean Imputation             Undersampling   
 47       Mean Imputation             Undersampling   
 48       Mean Imputation             Undersampling   
 49       Mean Imputation             Undersampling   
 50       Mean Imputation             Undersampling   
 51       Mean Imputation             Undersampling   
 52       Mean Imputation             Undersampling   
 53       Mean Imputation             Undersampling   
 54       Mean Imputation             Undersampling   
 55       Mean Imputation             Undersampling   
 56       Mean Imputation             Undersampling   
 57       Mean Imputation             Undersampling   
 58       Mean Imputation             Undersampling   
 59       Mean Imputation             Undersampling   
 60       Mean Imputation             Undersampling   
 61       Mean Imputation             Undersampling   
 62       Mean Imputation             Undersampling   
 63       Mean Imputation             Undersampling   
 64       Mean Imputation             Undersampling   
 65       Mean Imputation             Undersampling   
 66       Mean Imputation             Undersampling   
 67       Mean Imputation             Undersampling   
 68       Mean Imputation             Undersampling   
 69       Mean Imputation             Undersampling   
 70       Mean Imputation             Undersampling   
 71       Mean Imputation             Undersampling   
 72       Mean Imputation             Undersampling   
 73       Mean Imputation             Undersampling   
 74       Mean Imputation             Undersampling   
 75       Mean Imputation             Undersampling   
 76       Mean Imputation             Undersampling   
 77       Mean Imputation             Undersampling   
 78       Mean Imputation             Undersampling   
 79       Mean Imputation             Undersampling   
 80       Mean Imputation             Undersampling   
 81       Mean Imputation             Undersampling   
 82       Mean Imputation             Undersampling   
 83       Mean Imputation             Undersampling   
 84       Mean Imputation             Undersampling   
 85       Mean Imputation             Undersampling   
 86       Mean Imputation             Undersampling   
 87       Mean Imputation             Undersampling   
 88       Mean Imputation             Undersampling   
 89       Mean Imputation             Undersampling   
 90       Mean Imputation             Undersampling   
 91       Mean Imputation             Undersampling   
 92       Mean Imputation             Undersampling   
 93       Mean Imputation             Undersampling   
 94       Mean Imputation             Undersampling   
 95       Mean Imputation             Undersampling   
 96       Mean Imputation             Undersampling   
 97       Mean Imputation             Undersampling   
 98       Mean Imputation             Undersampling   
 99       Mean Imputation             Undersampling   
 100      Mean Imputation             Undersampling   
 101      Mean Imputation             Undersampling   
 102      Mean Imputation             Undersampling   
 103      Mean Imputation             Undersampling   
 104      Mean Imputation             Undersampling   
 105      Mean Imputation             Undersampling   
 106      Mean Imputation             Undersampling   
 107      Mean Imputation             Undersampling   
 108      Mean Imputation             Undersampling   
 109      Mean Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Undersampling_Me...  
 1    Support Vector Machines (SVM)_Undersampling_Me...  
 2    Support Vector Machines (SVM)_Undersampling_Me...  
 3    Support Vector Machines (SVM)_Undersampling_Me...  
 4    Support Vector Machines (SVM)_Undersampling_Me...  
 5    Support Vector Machines (SVM)_Undersampling_Me...  
 6    Support Vector Machines (SVM)_Undersampling_Me...  
 7    Support Vector Machines (SVM)_Undersampling_Me...  
 8    Support Vector Machines (SVM)_Undersampling_Me...  
 9    Support Vector Machines (SVM)_Undersampling_Me...  
 10   Support Vector Machines (SVM)_Undersampling_Me...  
 11   Support Vector Machines (SVM)_Undersampling_Me...  
 12   Support Vector Machines (SVM)_Undersampling_Me...  
 13   Support Vector Machines (SVM)_Undersampling_Me...  
 14   Support Vector Machines (SVM)_Undersampling_Me...  
 15   Support Vector Machines (SVM)_Undersampling_Me...  
 16   Support Vector Machines (SVM)_Undersampling_Me...  
 17   Support Vector Machines (SVM)_Undersampling_Me...  
 18   Support Vector Machines (SVM)_Undersampling_Me...  
 19   Support Vector Machines (SVM)_Undersampling_Me...  
 20   Support Vector Machines (SVM)_Undersampling_Me...  
 21   Support Vector Machines (SVM)_Undersampling_Me...  
 22   Support Vector Machines (SVM)_Undersampling_Me...  
 23   Support Vector Machines (SVM)_Undersampling_Me...  
 24   Support Vector Machines (SVM)_Undersampling_Me...  
 25   Support Vector Machines (SVM)_Undersampling_Me...  
 26   Support Vector Machines (SVM)_Undersampling_Me...  
 27   Support Vector Machines (SVM)_Undersampling_Me...  
 28   Support Vector Machines (SVM)_Undersampling_Me...  
 29   Support Vector Machines (SVM)_Undersampling_Me...  
 30   Support Vector Machines (SVM)_Undersampling_Me...  
 31   Support Vector Machines (SVM)_Undersampling_Me...  
 32   Support Vector Machines (SVM)_Undersampling_Me...  
 33   Support Vector Machines (SVM)_Undersampling_Me...  
 34   Support Vector Machines (SVM)_Undersampling_Me...  
 35   Support Vector Machines (SVM)_Undersampling_Me...  
 36   Support Vector Machines (SVM)_Undersampling_Me...  
 37   Support Vector Machines (SVM)_Undersampling_Me...  
 38   Support Vector Machines (SVM)_Undersampling_Me...  
 39   Support Vector Machines (SVM)_Undersampling_Me...  
 40   Support Vector Machines (SVM)_Undersampling_Me...  
 41   Support Vector Machines (SVM)_Undersampling_Me...  
 42   Support Vector Machines (SVM)_Undersampling_Me...  
 43   Support Vector Machines (SVM)_Undersampling_Me...  
 44   Support Vector Machines (SVM)_Undersampling_Me...  
 45   Support Vector Machines (SVM)_Undersampling_Me...  
 46   Support Vector Machines (SVM)_Undersampling_Me...  
 47   Support Vector Machines (SVM)_Undersampling_Me...  
 48   Support Vector Machines (SVM)_Undersampling_Me...  
 49   Support Vector Machines (SVM)_Undersampling_Me...  
 50   Support Vector Machines (SVM)_Undersampling_Me...  
 51   Support Vector Machines (SVM)_Undersampling_Me...  
 52   Support Vector Machines (SVM)_Undersampling_Me...  
 53   Support Vector Machines (SVM)_Undersampling_Me...  
 54   Support Vector Machines (SVM)_Undersampling_Me...  
 55   Support Vector Machines (SVM)_Undersampling_Me...  
 56   Support Vector Machines (SVM)_Undersampling_Me...  
 57   Support Vector Machines (SVM)_Undersampling_Me...  
 58   Support Vector Machines (SVM)_Undersampling_Me...  
 59   Support Vector Machines (SVM)_Undersampling_Me...  
 60   Support Vector Machines (SVM)_Undersampling_Me...  
 61   Support Vector Machines (SVM)_Undersampling_Me...  
 62   Support Vector Machines (SVM)_Undersampling_Me...  
 63   Support Vector Machines (SVM)_Undersampling_Me...  
 64   Support Vector Machines (SVM)_Undersampling_Me...  
 65   Support Vector Machines (SVM)_Undersampling_Me...  
 66   Support Vector Machines (SVM)_Undersampling_Me...  
 67   Support Vector Machines (SVM)_Undersampling_Me...  
 68   Support Vector Machines (SVM)_Undersampling_Me...  
 69   Support Vector Machines (SVM)_Undersampling_Me...  
 70   Support Vector Machines (SVM)_Undersampling_Me...  
 71   Support Vector Machines (SVM)_Undersampling_Me...  
 72   Support Vector Machines (SVM)_Undersampling_Me...  
 73   Support Vector Machines (SVM)_Undersampling_Me...  
 74   Support Vector Machines (SVM)_Undersampling_Me...  
 75   Support Vector Machines (SVM)_Undersampling_Me...  
 76   Support Vector Machines (SVM)_Undersampling_Me...  
 77   Support Vector Machines (SVM)_Undersampling_Me...  
 78   Support Vector Machines (SVM)_Undersampling_Me...  
 79   Support Vector Machines (SVM)_Undersampling_Me...  
 80   Support Vector Machines (SVM)_Undersampling_Me...  
 81   Support Vector Machines (SVM)_Undersampling_Me...  
 82   Support Vector Machines (SVM)_Undersampling_Me...  
 83   Support Vector Machines (SVM)_Undersampling_Me...  
 84   Support Vector Machines (SVM)_Undersampling_Me...  
 85   Support Vector Machines (SVM)_Undersampling_Me...  
 86   Support Vector Machines (SVM)_Undersampling_Me...  
 87   Support Vector Machines (SVM)_Undersampling_Me...  
 88   Support Vector Machines (SVM)_Undersampling_Me...  
 89   Support Vector Machines (SVM)_Undersampling_Me...  
 90   Support Vector Machines (SVM)_Undersampling_Me...  
 91   Support Vector Machines (SVM)_Undersampling_Me...  
 92   Support Vector Machines (SVM)_Undersampling_Me...  
 93   Support Vector Machines (SVM)_Undersampling_Me...  
 94   Support Vector Machines (SVM)_Undersampling_Me...  
 95   Support Vector Machines (SVM)_Undersampling_Me...  
 96   Support Vector Machines (SVM)_Undersampling_Me...  
 97   Support Vector Machines (SVM)_Undersampling_Me...  
 98   Support Vector Machines (SVM)_Undersampling_Me...  
 99   Support Vector Machines (SVM)_Undersampling_Me...  
 100  Support Vector Machines (SVM)_Undersampling_Me...  
 101  Support Vector Machines (SVM)_Undersampling_Me...  
 102  Support Vector Machines (SVM)_Undersampling_Me...  
 103  Support Vector Machines (SVM)_Undersampling_Me...  
 104  Support Vector Machines (SVM)_Undersampling_Me...  
 105  Support Vector Machines (SVM)_Undersampling_Me...  
 106  Support Vector Machines (SVM)_Undersampling_Me...  
 107  Support Vector Machines (SVM)_Undersampling_Me...  
 108  Support Vector Machines (SVM)_Undersampling_Me...  
 109  Support Vector Machines (SVM)_Undersampling_Me...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.280221             NaN   
 1    Support Vector Machines (SVM)  0.071429             NaN   
 2    Support Vector Machines (SVM)  0.877637             NaN   
 3    Support Vector Machines (SVM)  0.132105             NaN   
 4    Support Vector Machines (SVM)  0.617127             NaN   
 5    Support Vector Machines (SVM)  0.214396             NaN   
 6    Support Vector Machines (SVM)  0.234253             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.241770             NaN   
 23   Support Vector Machines (SVM)  0.049751             NaN   
 24   Support Vector Machines (SVM)  0.914634             NaN   
 25   Support Vector Machines (SVM)  0.094369             NaN   
 26   Support Vector Machines (SVM)  0.646067             NaN   
 27   Support Vector Machines (SVM)  0.276926             NaN   
 28   Support Vector Machines (SVM)  0.292134             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.306031             NaN   
 45   Support Vector Machines (SVM)  0.058442             NaN   
 46   Support Vector Machines (SVM)  0.866310             NaN   
 47   Support Vector Machines (SVM)  0.109496             NaN   
 48   Support Vector Machines (SVM)  0.588730             NaN   
 49   Support Vector Machines (SVM)  0.187069             NaN   
 50   Support Vector Machines (SVM)  0.177460             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.290832             NaN   
 67   Support Vector Machines (SVM)  0.060563             NaN   
 68   Support Vector Machines (SVM)  0.877551             NaN   
 69   Support Vector Machines (SVM)  0.113307             NaN   
 70   Support Vector Machines (SVM)  0.596604             NaN   
 71   Support Vector Machines (SVM)  0.231871             NaN   
 72   Support Vector Machines (SVM)  0.193207             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.288725             NaN   
 89   Support Vector Machines (SVM)  0.068150             NaN   
 90   Support Vector Machines (SVM)  0.907407             NaN   
 91   Support Vector Machines (SVM)  0.126779             NaN   
 92   Support Vector Machines (SVM)  0.614877             NaN   
 93   Support Vector Machines (SVM)  0.242732             NaN   
 94   Support Vector Machines (SVM)  0.229754             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0      Median Imputation             Undersampling   
 1      Median Imputation             Undersampling   
 2      Median Imputation             Undersampling   
 3      Median Imputation             Undersampling   
 4      Median Imputation             Undersampling   
 5      Median Imputation             Undersampling   
 6      Median Imputation             Undersampling   
 7      Median Imputation             Undersampling   
 8      Median Imputation             Undersampling   
 9      Median Imputation             Undersampling   
 10     Median Imputation             Undersampling   
 11     Median Imputation             Undersampling   
 12     Median Imputation             Undersampling   
 13     Median Imputation             Undersampling   
 14     Median Imputation             Undersampling   
 15     Median Imputation             Undersampling   
 16     Median Imputation             Undersampling   
 17     Median Imputation             Undersampling   
 18     Median Imputation             Undersampling   
 19     Median Imputation             Undersampling   
 20     Median Imputation             Undersampling   
 21     Median Imputation             Undersampling   
 22     Median Imputation             Undersampling   
 23     Median Imputation             Undersampling   
 24     Median Imputation             Undersampling   
 25     Median Imputation             Undersampling   
 26     Median Imputation             Undersampling   
 27     Median Imputation             Undersampling   
 28     Median Imputation             Undersampling   
 29     Median Imputation             Undersampling   
 30     Median Imputation             Undersampling   
 31     Median Imputation             Undersampling   
 32     Median Imputation             Undersampling   
 33     Median Imputation             Undersampling   
 34     Median Imputation             Undersampling   
 35     Median Imputation             Undersampling   
 36     Median Imputation             Undersampling   
 37     Median Imputation             Undersampling   
 38     Median Imputation             Undersampling   
 39     Median Imputation             Undersampling   
 40     Median Imputation             Undersampling   
 41     Median Imputation             Undersampling   
 42     Median Imputation             Undersampling   
 43     Median Imputation             Undersampling   
 44     Median Imputation             Undersampling   
 45     Median Imputation             Undersampling   
 46     Median Imputation             Undersampling   
 47     Median Imputation             Undersampling   
 48     Median Imputation             Undersampling   
 49     Median Imputation             Undersampling   
 50     Median Imputation             Undersampling   
 51     Median Imputation             Undersampling   
 52     Median Imputation             Undersampling   
 53     Median Imputation             Undersampling   
 54     Median Imputation             Undersampling   
 55     Median Imputation             Undersampling   
 56     Median Imputation             Undersampling   
 57     Median Imputation             Undersampling   
 58     Median Imputation             Undersampling   
 59     Median Imputation             Undersampling   
 60     Median Imputation             Undersampling   
 61     Median Imputation             Undersampling   
 62     Median Imputation             Undersampling   
 63     Median Imputation             Undersampling   
 64     Median Imputation             Undersampling   
 65     Median Imputation             Undersampling   
 66     Median Imputation             Undersampling   
 67     Median Imputation             Undersampling   
 68     Median Imputation             Undersampling   
 69     Median Imputation             Undersampling   
 70     Median Imputation             Undersampling   
 71     Median Imputation             Undersampling   
 72     Median Imputation             Undersampling   
 73     Median Imputation             Undersampling   
 74     Median Imputation             Undersampling   
 75     Median Imputation             Undersampling   
 76     Median Imputation             Undersampling   
 77     Median Imputation             Undersampling   
 78     Median Imputation             Undersampling   
 79     Median Imputation             Undersampling   
 80     Median Imputation             Undersampling   
 81     Median Imputation             Undersampling   
 82     Median Imputation             Undersampling   
 83     Median Imputation             Undersampling   
 84     Median Imputation             Undersampling   
 85     Median Imputation             Undersampling   
 86     Median Imputation             Undersampling   
 87     Median Imputation             Undersampling   
 88     Median Imputation             Undersampling   
 89     Median Imputation             Undersampling   
 90     Median Imputation             Undersampling   
 91     Median Imputation             Undersampling   
 92     Median Imputation             Undersampling   
 93     Median Imputation             Undersampling   
 94     Median Imputation             Undersampling   
 95     Median Imputation             Undersampling   
 96     Median Imputation             Undersampling   
 97     Median Imputation             Undersampling   
 98     Median Imputation             Undersampling   
 99     Median Imputation             Undersampling   
 100    Median Imputation             Undersampling   
 101    Median Imputation             Undersampling   
 102    Median Imputation             Undersampling   
 103    Median Imputation             Undersampling   
 104    Median Imputation             Undersampling   
 105    Median Imputation             Undersampling   
 106    Median Imputation             Undersampling   
 107    Median Imputation             Undersampling   
 108    Median Imputation             Undersampling   
 109    Median Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Undersampling_Me...  
 1    Support Vector Machines (SVM)_Undersampling_Me...  
 2    Support Vector Machines (SVM)_Undersampling_Me...  
 3    Support Vector Machines (SVM)_Undersampling_Me...  
 4    Support Vector Machines (SVM)_Undersampling_Me...  
 5    Support Vector Machines (SVM)_Undersampling_Me...  
 6    Support Vector Machines (SVM)_Undersampling_Me...  
 7    Support Vector Machines (SVM)_Undersampling_Me...  
 8    Support Vector Machines (SVM)_Undersampling_Me...  
 9    Support Vector Machines (SVM)_Undersampling_Me...  
 10   Support Vector Machines (SVM)_Undersampling_Me...  
 11   Support Vector Machines (SVM)_Undersampling_Me...  
 12   Support Vector Machines (SVM)_Undersampling_Me...  
 13   Support Vector Machines (SVM)_Undersampling_Me...  
 14   Support Vector Machines (SVM)_Undersampling_Me...  
 15   Support Vector Machines (SVM)_Undersampling_Me...  
 16   Support Vector Machines (SVM)_Undersampling_Me...  
 17   Support Vector Machines (SVM)_Undersampling_Me...  
 18   Support Vector Machines (SVM)_Undersampling_Me...  
 19   Support Vector Machines (SVM)_Undersampling_Me...  
 20   Support Vector Machines (SVM)_Undersampling_Me...  
 21   Support Vector Machines (SVM)_Undersampling_Me...  
 22   Support Vector Machines (SVM)_Undersampling_Me...  
 23   Support Vector Machines (SVM)_Undersampling_Me...  
 24   Support Vector Machines (SVM)_Undersampling_Me...  
 25   Support Vector Machines (SVM)_Undersampling_Me...  
 26   Support Vector Machines (SVM)_Undersampling_Me...  
 27   Support Vector Machines (SVM)_Undersampling_Me...  
 28   Support Vector Machines (SVM)_Undersampling_Me...  
 29   Support Vector Machines (SVM)_Undersampling_Me...  
 30   Support Vector Machines (SVM)_Undersampling_Me...  
 31   Support Vector Machines (SVM)_Undersampling_Me...  
 32   Support Vector Machines (SVM)_Undersampling_Me...  
 33   Support Vector Machines (SVM)_Undersampling_Me...  
 34   Support Vector Machines (SVM)_Undersampling_Me...  
 35   Support Vector Machines (SVM)_Undersampling_Me...  
 36   Support Vector Machines (SVM)_Undersampling_Me...  
 37   Support Vector Machines (SVM)_Undersampling_Me...  
 38   Support Vector Machines (SVM)_Undersampling_Me...  
 39   Support Vector Machines (SVM)_Undersampling_Me...  
 40   Support Vector Machines (SVM)_Undersampling_Me...  
 41   Support Vector Machines (SVM)_Undersampling_Me...  
 42   Support Vector Machines (SVM)_Undersampling_Me...  
 43   Support Vector Machines (SVM)_Undersampling_Me...  
 44   Support Vector Machines (SVM)_Undersampling_Me...  
 45   Support Vector Machines (SVM)_Undersampling_Me...  
 46   Support Vector Machines (SVM)_Undersampling_Me...  
 47   Support Vector Machines (SVM)_Undersampling_Me...  
 48   Support Vector Machines (SVM)_Undersampling_Me...  
 49   Support Vector Machines (SVM)_Undersampling_Me...  
 50   Support Vector Machines (SVM)_Undersampling_Me...  
 51   Support Vector Machines (SVM)_Undersampling_Me...  
 52   Support Vector Machines (SVM)_Undersampling_Me...  
 53   Support Vector Machines (SVM)_Undersampling_Me...  
 54   Support Vector Machines (SVM)_Undersampling_Me...  
 55   Support Vector Machines (SVM)_Undersampling_Me...  
 56   Support Vector Machines (SVM)_Undersampling_Me...  
 57   Support Vector Machines (SVM)_Undersampling_Me...  
 58   Support Vector Machines (SVM)_Undersampling_Me...  
 59   Support Vector Machines (SVM)_Undersampling_Me...  
 60   Support Vector Machines (SVM)_Undersampling_Me...  
 61   Support Vector Machines (SVM)_Undersampling_Me...  
 62   Support Vector Machines (SVM)_Undersampling_Me...  
 63   Support Vector Machines (SVM)_Undersampling_Me...  
 64   Support Vector Machines (SVM)_Undersampling_Me...  
 65   Support Vector Machines (SVM)_Undersampling_Me...  
 66   Support Vector Machines (SVM)_Undersampling_Me...  
 67   Support Vector Machines (SVM)_Undersampling_Me...  
 68   Support Vector Machines (SVM)_Undersampling_Me...  
 69   Support Vector Machines (SVM)_Undersampling_Me...  
 70   Support Vector Machines (SVM)_Undersampling_Me...  
 71   Support Vector Machines (SVM)_Undersampling_Me...  
 72   Support Vector Machines (SVM)_Undersampling_Me...  
 73   Support Vector Machines (SVM)_Undersampling_Me...  
 74   Support Vector Machines (SVM)_Undersampling_Me...  
 75   Support Vector Machines (SVM)_Undersampling_Me...  
 76   Support Vector Machines (SVM)_Undersampling_Me...  
 77   Support Vector Machines (SVM)_Undersampling_Me...  
 78   Support Vector Machines (SVM)_Undersampling_Me...  
 79   Support Vector Machines (SVM)_Undersampling_Me...  
 80   Support Vector Machines (SVM)_Undersampling_Me...  
 81   Support Vector Machines (SVM)_Undersampling_Me...  
 82   Support Vector Machines (SVM)_Undersampling_Me...  
 83   Support Vector Machines (SVM)_Undersampling_Me...  
 84   Support Vector Machines (SVM)_Undersampling_Me...  
 85   Support Vector Machines (SVM)_Undersampling_Me...  
 86   Support Vector Machines (SVM)_Undersampling_Me...  
 87   Support Vector Machines (SVM)_Undersampling_Me...  
 88   Support Vector Machines (SVM)_Undersampling_Me...  
 89   Support Vector Machines (SVM)_Undersampling_Me...  
 90   Support Vector Machines (SVM)_Undersampling_Me...  
 91   Support Vector Machines (SVM)_Undersampling_Me...  
 92   Support Vector Machines (SVM)_Undersampling_Me...  
 93   Support Vector Machines (SVM)_Undersampling_Me...  
 94   Support Vector Machines (SVM)_Undersampling_Me...  
 95   Support Vector Machines (SVM)_Undersampling_Me...  
 96   Support Vector Machines (SVM)_Undersampling_Me...  
 97   Support Vector Machines (SVM)_Undersampling_Me...  
 98   Support Vector Machines (SVM)_Undersampling_Me...  
 99   Support Vector Machines (SVM)_Undersampling_Me...  
 100  Support Vector Machines (SVM)_Undersampling_Me...  
 101  Support Vector Machines (SVM)_Undersampling_Me...  
 102  Support Vector Machines (SVM)_Undersampling_Me...  
 103  Support Vector Machines (SVM)_Undersampling_Me...  
 104  Support Vector Machines (SVM)_Undersampling_Me...  
 105  Support Vector Machines (SVM)_Undersampling_Me...  
 106  Support Vector Machines (SVM)_Undersampling_Me...  
 107  Support Vector Machines (SVM)_Undersampling_Me...  
 108  Support Vector Machines (SVM)_Undersampling_Me...  
 109  Support Vector Machines (SVM)_Undersampling_Me...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.355017             NaN   
 1    Support Vector Machines (SVM)  0.076894             NaN   
 2    Support Vector Machines (SVM)  0.848101             NaN   
 3    Support Vector Machines (SVM)  0.141003             NaN   
 4    Support Vector Machines (SVM)  0.641670             NaN   
 5    Support Vector Machines (SVM)  0.229685             NaN   
 6    Support Vector Machines (SVM)  0.283340             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.331841             NaN   
 23   Support Vector Machines (SVM)  0.054784             NaN   
 24   Support Vector Machines (SVM)  0.890244             NaN   
 25   Support Vector Machines (SVM)  0.103217             NaN   
 26   Support Vector Machines (SVM)  0.667777             NaN   
 27   Support Vector Machines (SVM)  0.273460             NaN   
 28   Support Vector Machines (SVM)  0.335554             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.345273             NaN   
 45   Support Vector Machines (SVM)  0.059747             NaN   
 46   Support Vector Machines (SVM)  0.834225             NaN   
 47   Support Vector Machines (SVM)  0.111508             NaN   
 48   Support Vector Machines (SVM)  0.652221             NaN   
 49   Support Vector Machines (SVM)  0.265315             NaN   
 50   Support Vector Machines (SVM)  0.304441             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.328504             NaN   
 67   Support Vector Machines (SVM)  0.064421             NaN   
 68   Support Vector Machines (SVM)  0.887755             NaN   
 69   Support Vector Machines (SVM)  0.120124             NaN   
 70   Support Vector Machines (SVM)  0.647746             NaN   
 71   Support Vector Machines (SVM)  0.237738             NaN   
 72   Support Vector Machines (SVM)  0.295492             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.337724             NaN   
 89   Support Vector Machines (SVM)  0.069985             NaN   
 90   Support Vector Machines (SVM)  0.865741             NaN   
 91   Support Vector Machines (SVM)  0.129501             NaN   
 92   Support Vector Machines (SVM)  0.673923             NaN   
 93   Support Vector Machines (SVM)  0.290306             NaN   
 94   Support Vector Machines (SVM)  0.347846             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 1        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 2        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 3        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 4        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 5        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 6        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 7        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 8        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 9        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 10       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 11       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 12       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 13       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 14       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 15       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 16       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 17       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 18       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 19       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 20       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 21       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 22       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 23       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 24       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 25       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 26       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 27       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 28       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 29       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 30       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 31       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 32       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 33       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 34       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 35       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 36       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 37       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 38       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 39       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 40       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 41       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 42       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 43       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 44       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 45       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 46       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 47       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 48       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 49       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 50       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 51       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 52       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 53       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 54       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 55       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 56       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 57       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 58       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 59       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 60       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 61       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 62       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 63       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 64       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 65       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 66       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 67       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 68       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 69       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 70       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 71       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 72       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 73       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 74       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 75       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 76       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 77       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 78       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 79       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 80       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 81       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 82       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 83       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 84       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 85       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 86       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 87       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 88       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 89       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 90       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 91       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 92       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 93       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 94       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 95       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 96       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 97       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 98       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 99       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 100      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 101      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 102      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 103      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 104      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 105      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 106      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 107      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 108      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 109      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.203582             NaN   
 1    Support Vector Machines (SVM)  0.066563             NaN   
 2    Support Vector Machines (SVM)  0.902954             NaN   
 3    Support Vector Machines (SVM)  0.123986             NaN   
 4    Support Vector Machines (SVM)  0.622080             NaN   
 5    Support Vector Machines (SVM)  0.219860             NaN   
 6    Support Vector Machines (SVM)  0.244159             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.185673             NaN   
 23   Support Vector Machines (SVM)  0.048427             NaN   
 24   Support Vector Machines (SVM)  0.957317             NaN   
 25   Support Vector Machines (SVM)  0.092190             NaN   
 26   Support Vector Machines (SVM)  0.642406             NaN   
 27   Support Vector Machines (SVM)  0.263607             NaN   
 28   Support Vector Machines (SVM)  0.284813             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.201475             NaN   
 45   Support Vector Machines (SVM)  0.055330             NaN   
 46   Support Vector Machines (SVM)  0.946524             NaN   
 47   Support Vector Machines (SVM)  0.104548             NaN   
 48   Support Vector Machines (SVM)  0.620065             NaN   
 49   Support Vector Machines (SVM)  0.257247             NaN   
 50   Support Vector Machines (SVM)  0.240129             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.176502             NaN   
 67   Support Vector Machines (SVM)  0.056866             NaN   
 68   Support Vector Machines (SVM)  0.959184             NaN   
 69   Support Vector Machines (SVM)  0.107367             NaN   
 70   Support Vector Machines (SVM)  0.609985             NaN   
 71   Support Vector Machines (SVM)  0.192829             NaN   
 72   Support Vector Machines (SVM)  0.219970             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.199420             NaN   
 89   Support Vector Machines (SVM)  0.063679             NaN   
 90   Support Vector Machines (SVM)  0.953704             NaN   
 91   Support Vector Machines (SVM)  0.119386             NaN   
 92   Support Vector Machines (SVM)  0.638917             NaN   
 93   Support Vector Machines (SVM)  0.249591             NaN   
 94   Support Vector Machines (SVM)  0.277835             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 1        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 2        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 3        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 4        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 5        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 6        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 7        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 8        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 9        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 10       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 11       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 12       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 13       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 14       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 15       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 16       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 17       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 18       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 19       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 20       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 21       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 22       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 23       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 24       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 25       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 26       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 27       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 28       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 29       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 30       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 31       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 32       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 33       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 34       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 35       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 36       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 37       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 38       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 39       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 40       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 41       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 42       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 43       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 44       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 45       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 46       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 47       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 48       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 49       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 50       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 51       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 52       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 53       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 54       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 55       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 56       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 57       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 58       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 59       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 60       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 61       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 62       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 63       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 64       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 65       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 66       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 67       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 68       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 69       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 70       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 71       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 72       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 73       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 74       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 75       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 76       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 77       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 78       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 79       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 80       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 81       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 82       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 83       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 84       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 85       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 86       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 87       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 88       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 89       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 90       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 91       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 92       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 93       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 94       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 95       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 96       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 97       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 98       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 99       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 100      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 101      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 102      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 103      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 104      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 105      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 106      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 107      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 108      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 109      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.309718             NaN   
 1    Support Vector Machines (SVM)  0.074590             NaN   
 2    Support Vector Machines (SVM)  0.881857             NaN   
 3    Support Vector Machines (SVM)  0.137545             NaN   
 4    Support Vector Machines (SVM)  0.637241             NaN   
 5    Support Vector Machines (SVM)  0.222532             NaN   
 6    Support Vector Machines (SVM)  0.274482             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.290756             NaN   
 23   Support Vector Machines (SVM)  0.052389             NaN   
 24   Support Vector Machines (SVM)  0.902439             NaN   
 25   Support Vector Machines (SVM)  0.099030             NaN   
 26   Support Vector Machines (SVM)  0.652181             NaN   
 27   Support Vector Machines (SVM)  0.268132             NaN   
 28   Support Vector Machines (SVM)  0.304363             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.285489             NaN   
 45   Support Vector Machines (SVM)  0.057773             NaN   
 46   Support Vector Machines (SVM)  0.882353             NaN   
 47   Support Vector Machines (SVM)  0.108446             NaN   
 48   Support Vector Machines (SVM)  0.640422             NaN   
 49   Support Vector Machines (SVM)  0.239938             NaN   
 50   Support Vector Machines (SVM)  0.280845             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.276870             NaN   
 67   Support Vector Machines (SVM)  0.060669             NaN   
 68   Support Vector Machines (SVM)  0.897959             NaN   
 69   Support Vector Machines (SVM)  0.113658             NaN   
 70   Support Vector Machines (SVM)  0.627589             NaN   
 71   Support Vector Machines (SVM)  0.231043             NaN   
 72   Support Vector Machines (SVM)  0.255177             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.295838             NaN   
 89   Support Vector Machines (SVM)  0.067277             NaN   
 90   Support Vector Machines (SVM)  0.884259             NaN   
 91   Support Vector Machines (SVM)  0.125041             NaN   
 92   Support Vector Machines (SVM)  0.666149             NaN   
 93   Support Vector Machines (SVM)  0.283090             NaN   
 94   Support Vector Machines (SVM)  0.332297             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0      Median Imputation  Hybrid Sampling (SMOTEENN)   
 1      Median Imputation  Hybrid Sampling (SMOTEENN)   
 2      Median Imputation  Hybrid Sampling (SMOTEENN)   
 3      Median Imputation  Hybrid Sampling (SMOTEENN)   
 4      Median Imputation  Hybrid Sampling (SMOTEENN)   
 5      Median Imputation  Hybrid Sampling (SMOTEENN)   
 6      Median Imputation  Hybrid Sampling (SMOTEENN)   
 7      Median Imputation  Hybrid Sampling (SMOTEENN)   
 8      Median Imputation  Hybrid Sampling (SMOTEENN)   
 9      Median Imputation  Hybrid Sampling (SMOTEENN)   
 10     Median Imputation  Hybrid Sampling (SMOTEENN)   
 11     Median Imputation  Hybrid Sampling (SMOTEENN)   
 12     Median Imputation  Hybrid Sampling (SMOTEENN)   
 13     Median Imputation  Hybrid Sampling (SMOTEENN)   
 14     Median Imputation  Hybrid Sampling (SMOTEENN)   
 15     Median Imputation  Hybrid Sampling (SMOTEENN)   
 16     Median Imputation  Hybrid Sampling (SMOTEENN)   
 17     Median Imputation  Hybrid Sampling (SMOTEENN)   
 18     Median Imputation  Hybrid Sampling (SMOTEENN)   
 19     Median Imputation  Hybrid Sampling (SMOTEENN)   
 20     Median Imputation  Hybrid Sampling (SMOTEENN)   
 21     Median Imputation  Hybrid Sampling (SMOTEENN)   
 22     Median Imputation  Hybrid Sampling (SMOTEENN)   
 23     Median Imputation  Hybrid Sampling (SMOTEENN)   
 24     Median Imputation  Hybrid Sampling (SMOTEENN)   
 25     Median Imputation  Hybrid Sampling (SMOTEENN)   
 26     Median Imputation  Hybrid Sampling (SMOTEENN)   
 27     Median Imputation  Hybrid Sampling (SMOTEENN)   
 28     Median Imputation  Hybrid Sampling (SMOTEENN)   
 29     Median Imputation  Hybrid Sampling (SMOTEENN)   
 30     Median Imputation  Hybrid Sampling (SMOTEENN)   
 31     Median Imputation  Hybrid Sampling (SMOTEENN)   
 32     Median Imputation  Hybrid Sampling (SMOTEENN)   
 33     Median Imputation  Hybrid Sampling (SMOTEENN)   
 34     Median Imputation  Hybrid Sampling (SMOTEENN)   
 35     Median Imputation  Hybrid Sampling (SMOTEENN)   
 36     Median Imputation  Hybrid Sampling (SMOTEENN)   
 37     Median Imputation  Hybrid Sampling (SMOTEENN)   
 38     Median Imputation  Hybrid Sampling (SMOTEENN)   
 39     Median Imputation  Hybrid Sampling (SMOTEENN)   
 40     Median Imputation  Hybrid Sampling (SMOTEENN)   
 41     Median Imputation  Hybrid Sampling (SMOTEENN)   
 42     Median Imputation  Hybrid Sampling (SMOTEENN)   
 43     Median Imputation  Hybrid Sampling (SMOTEENN)   
 44     Median Imputation  Hybrid Sampling (SMOTEENN)   
 45     Median Imputation  Hybrid Sampling (SMOTEENN)   
 46     Median Imputation  Hybrid Sampling (SMOTEENN)   
 47     Median Imputation  Hybrid Sampling (SMOTEENN)   
 48     Median Imputation  Hybrid Sampling (SMOTEENN)   
 49     Median Imputation  Hybrid Sampling (SMOTEENN)   
 50     Median Imputation  Hybrid Sampling (SMOTEENN)   
 51     Median Imputation  Hybrid Sampling (SMOTEENN)   
 52     Median Imputation  Hybrid Sampling (SMOTEENN)   
 53     Median Imputation  Hybrid Sampling (SMOTEENN)   
 54     Median Imputation  Hybrid Sampling (SMOTEENN)   
 55     Median Imputation  Hybrid Sampling (SMOTEENN)   
 56     Median Imputation  Hybrid Sampling (SMOTEENN)   
 57     Median Imputation  Hybrid Sampling (SMOTEENN)   
 58     Median Imputation  Hybrid Sampling (SMOTEENN)   
 59     Median Imputation  Hybrid Sampling (SMOTEENN)   
 60     Median Imputation  Hybrid Sampling (SMOTEENN)   
 61     Median Imputation  Hybrid Sampling (SMOTEENN)   
 62     Median Imputation  Hybrid Sampling (SMOTEENN)   
 63     Median Imputation  Hybrid Sampling (SMOTEENN)   
 64     Median Imputation  Hybrid Sampling (SMOTEENN)   
 65     Median Imputation  Hybrid Sampling (SMOTEENN)   
 66     Median Imputation  Hybrid Sampling (SMOTEENN)   
 67     Median Imputation  Hybrid Sampling (SMOTEENN)   
 68     Median Imputation  Hybrid Sampling (SMOTEENN)   
 69     Median Imputation  Hybrid Sampling (SMOTEENN)   
 70     Median Imputation  Hybrid Sampling (SMOTEENN)   
 71     Median Imputation  Hybrid Sampling (SMOTEENN)   
 72     Median Imputation  Hybrid Sampling (SMOTEENN)   
 73     Median Imputation  Hybrid Sampling (SMOTEENN)   
 74     Median Imputation  Hybrid Sampling (SMOTEENN)   
 75     Median Imputation  Hybrid Sampling (SMOTEENN)   
 76     Median Imputation  Hybrid Sampling (SMOTEENN)   
 77     Median Imputation  Hybrid Sampling (SMOTEENN)   
 78     Median Imputation  Hybrid Sampling (SMOTEENN)   
 79     Median Imputation  Hybrid Sampling (SMOTEENN)   
 80     Median Imputation  Hybrid Sampling (SMOTEENN)   
 81     Median Imputation  Hybrid Sampling (SMOTEENN)   
 82     Median Imputation  Hybrid Sampling (SMOTEENN)   
 83     Median Imputation  Hybrid Sampling (SMOTEENN)   
 84     Median Imputation  Hybrid Sampling (SMOTEENN)   
 85     Median Imputation  Hybrid Sampling (SMOTEENN)   
 86     Median Imputation  Hybrid Sampling (SMOTEENN)   
 87     Median Imputation  Hybrid Sampling (SMOTEENN)   
 88     Median Imputation  Hybrid Sampling (SMOTEENN)   
 89     Median Imputation  Hybrid Sampling (SMOTEENN)   
 90     Median Imputation  Hybrid Sampling (SMOTEENN)   
 91     Median Imputation  Hybrid Sampling (SMOTEENN)   
 92     Median Imputation  Hybrid Sampling (SMOTEENN)   
 93     Median Imputation  Hybrid Sampling (SMOTEENN)   
 94     Median Imputation  Hybrid Sampling (SMOTEENN)   
 95     Median Imputation  Hybrid Sampling (SMOTEENN)   
 96     Median Imputation  Hybrid Sampling (SMOTEENN)   
 97     Median Imputation  Hybrid Sampling (SMOTEENN)   
 98     Median Imputation  Hybrid Sampling (SMOTEENN)   
 99     Median Imputation  Hybrid Sampling (SMOTEENN)   
 100    Median Imputation  Hybrid Sampling (SMOTEENN)   
 101    Median Imputation  Hybrid Sampling (SMOTEENN)   
 102    Median Imputation  Hybrid Sampling (SMOTEENN)   
 103    Median Imputation  Hybrid Sampling (SMOTEENN)   
 104    Median Imputation  Hybrid Sampling (SMOTEENN)   
 105    Median Imputation  Hybrid Sampling (SMOTEENN)   
 106    Median Imputation  Hybrid Sampling (SMOTEENN)   
 107    Median Imputation  Hybrid Sampling (SMOTEENN)   
 108    Median Imputation  Hybrid Sampling (SMOTEENN)   
 109    Median Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.501185             NaN   
 1    Support Vector Machines (SVM)  0.085957             NaN   
 2    Support Vector Machines (SVM)  0.725738             NaN   
 3    Support Vector Machines (SVM)  0.153709             NaN   
 4    Support Vector Machines (SVM)  0.640596             NaN   
 5    Support Vector Machines (SVM)  0.214162             NaN   
 6    Support Vector Machines (SVM)  0.281192             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.488544             NaN   
 23   Support Vector Machines (SVM)  0.062500             NaN   
 24   Support Vector Machines (SVM)  0.774390             NaN   
 25   Support Vector Machines (SVM)  0.115665             NaN   
 26   Support Vector Machines (SVM)  0.680118             NaN   
 27   Support Vector Machines (SVM)  0.273133             NaN   
 28   Support Vector Machines (SVM)  0.360236             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.476165             NaN   
 45   Support Vector Machines (SVM)  0.067658             NaN   
 46   Support Vector Machines (SVM)  0.754011             NaN   
 47   Support Vector Machines (SVM)  0.124174             NaN   
 48   Support Vector Machines (SVM)  0.651016             NaN   
 49   Support Vector Machines (SVM)  0.226668             NaN   
 50   Support Vector Machines (SVM)  0.302032             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.474447             NaN   
 67   Support Vector Machines (SVM)  0.068171             NaN   
 68   Support Vector Machines (SVM)  0.724490             NaN   
 69   Support Vector Machines (SVM)  0.124616             NaN   
 70   Support Vector Machines (SVM)  0.646269             NaN   
 71   Support Vector Machines (SVM)  0.215901             NaN   
 72   Support Vector Machines (SVM)  0.292538             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.500263             NaN   
 89   Support Vector Machines (SVM)  0.079119             NaN   
 90   Support Vector Machines (SVM)  0.731481             NaN   
 91   Support Vector Machines (SVM)  0.142793             NaN   
 92   Support Vector Machines (SVM)  0.666259             NaN   
 93   Support Vector Machines (SVM)  0.270945             NaN   
 94   Support Vector Machines (SVM)  0.332519             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 1        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 2        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 3        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 4        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 5        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 6        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 7        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 8        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 9        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 10       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 11       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 12       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 13       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 14       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 15       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 16       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 17       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 18       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 19       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 20       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 21       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 22       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 23       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 24       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 25       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 26       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 27       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 28       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 29       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 30       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 31       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 32       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 33       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 34       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 35       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 36       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 37       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 38       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 39       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 40       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 41       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 42       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 43       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 44       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 45       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 46       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 47       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 48       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 49       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 50       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 51       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 52       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 53       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 54       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 55       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 56       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 57       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 58       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 59       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 60       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 61       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 62       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 63       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 64       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 65       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 66       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 67       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 68       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 69       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 70       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 71       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 72       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 73       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 74       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 75       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 76       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 77       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 78       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 79       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 80       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 81       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 82       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 83       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 84       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 85       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 86       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 87       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 88       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 89       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 90       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 91       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 92       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 93       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 94       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 95       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 96       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 97       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 98       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 99       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 100      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 101      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 102      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 103      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 104      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 105      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 106      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 107      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 108      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 109      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.350277             NaN   
 1    Support Vector Machines (SVM)  0.073777             NaN   
 2    Support Vector Machines (SVM)  0.814346             NaN   
 3    Support Vector Machines (SVM)  0.135296             NaN   
 4    Support Vector Machines (SVM)  0.609686             NaN   
 5    Support Vector Machines (SVM)  0.165270             NaN   
 6    Support Vector Machines (SVM)  0.219371             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.325784             NaN   
 23   Support Vector Machines (SVM)  0.052651             NaN   
 24   Support Vector Machines (SVM)  0.859756             NaN   
 25   Support Vector Machines (SVM)  0.099226             NaN   
 26   Support Vector Machines (SVM)  0.650485             NaN   
 27   Support Vector Machines (SVM)  0.260560             NaN   
 28   Support Vector Machines (SVM)  0.300969             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.339742             NaN   
 45   Support Vector Machines (SVM)  0.058264             NaN   
 46   Support Vector Machines (SVM)  0.818182             NaN   
 47   Support Vector Machines (SVM)  0.108781             NaN   
 48   Support Vector Machines (SVM)  0.622146             NaN   
 49   Support Vector Machines (SVM)  0.212454             NaN   
 50   Support Vector Machines (SVM)  0.244292             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.304268             NaN   
 67   Support Vector Machines (SVM)  0.060410             NaN   
 68   Support Vector Machines (SVM)  0.857143             NaN   
 69   Support Vector Machines (SVM)  0.112865             NaN   
 70   Support Vector Machines (SVM)  0.626279             NaN   
 71   Support Vector Machines (SVM)  0.203997             NaN   
 72   Support Vector Machines (SVM)  0.252558             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.397787             NaN   
 89   Support Vector Machines (SVM)  0.074074             NaN   
 90   Support Vector Machines (SVM)  0.833333             NaN   
 91   Support Vector Machines (SVM)  0.136054             NaN   
 92   Support Vector Machines (SVM)  0.638092             NaN   
 93   Support Vector Machines (SVM)  0.222714             NaN   
 94   Support Vector Machines (SVM)  0.276183             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 1        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 2        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 3        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 4        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 5        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 6        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 7        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 8        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 9        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 10       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 11       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 12       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 13       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 14       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 15       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 16       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 17       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 18       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 19       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 20       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 21       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 22       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 23       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 24       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 25       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 26       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 27       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 28       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 29       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 30       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 31       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 32       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 33       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 34       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 35       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 36       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 37       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 38       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 39       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 40       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 41       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 42       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 43       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 44       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 45       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 46       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 47       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 48       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 49       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 50       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 51       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 52       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 53       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 54       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 55       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 56       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 57       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 58       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 59       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 60       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 61       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 62       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 63       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 64       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 65       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 66       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 67       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 68       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 69       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 70       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 71       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 72       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 73       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 74       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 75       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 76       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 77       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 78       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 79       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 80       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 81       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 82       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 83       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 84       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 85       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 86       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 87       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 88       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 89       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 90       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 91       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 92       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 93       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 94       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 95       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 96       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 97       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 98       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 99       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 100      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 101      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 102      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 103      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 104      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 105      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 106      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 107      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 108      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 109      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
                          Algorithm   Metrics Hyperparameters  \
 0    Support Vector Machines (SVM)  0.412694             NaN   
 1    Support Vector Machines (SVM)  0.080421             NaN   
 2    Support Vector Machines (SVM)  0.805907             NaN   
 3    Support Vector Machines (SVM)  0.146248             NaN   
 4    Support Vector Machines (SVM)  0.638190             NaN   
 5    Support Vector Machines (SVM)  0.232900             NaN   
 6    Support Vector Machines (SVM)  0.276380             NaN   
 7    Support Vector Machines (SVM)       NaN             1.0   
 8    Support Vector Machines (SVM)       NaN           False   
 9    Support Vector Machines (SVM)       NaN             200   
 10   Support Vector Machines (SVM)       NaN            None   
 11   Support Vector Machines (SVM)       NaN             0.0   
 12   Support Vector Machines (SVM)       NaN             ovr   
 13   Support Vector Machines (SVM)       NaN               3   
 14   Support Vector Machines (SVM)       NaN           scale   
 15   Support Vector Machines (SVM)       NaN             rbf   
 16   Support Vector Machines (SVM)       NaN              -1   
 17   Support Vector Machines (SVM)       NaN            True   
 18   Support Vector Machines (SVM)       NaN            None   
 19   Support Vector Machines (SVM)       NaN            True   
 20   Support Vector Machines (SVM)       NaN           0.001   
 21   Support Vector Machines (SVM)       NaN           False   
 22   Support Vector Machines (SVM)  0.391888             NaN   
 23   Support Vector Machines (SVM)  0.056268             NaN   
 24   Support Vector Machines (SVM)  0.829268             NaN   
 25   Support Vector Machines (SVM)  0.105386             NaN   
 26   Support Vector Machines (SVM)  0.658811             NaN   
 27   Support Vector Machines (SVM)  0.281176             NaN   
 28   Support Vector Machines (SVM)  0.317622             NaN   
 29   Support Vector Machines (SVM)       NaN             1.0   
 30   Support Vector Machines (SVM)       NaN           False   
 31   Support Vector Machines (SVM)       NaN             200   
 32   Support Vector Machines (SVM)       NaN            None   
 33   Support Vector Machines (SVM)       NaN             0.0   
 34   Support Vector Machines (SVM)       NaN             ovr   
 35   Support Vector Machines (SVM)       NaN               3   
 36   Support Vector Machines (SVM)       NaN           scale   
 37   Support Vector Machines (SVM)       NaN             rbf   
 38   Support Vector Machines (SVM)       NaN              -1   
 39   Support Vector Machines (SVM)       NaN            True   
 40   Support Vector Machines (SVM)       NaN            None   
 41   Support Vector Machines (SVM)       NaN            True   
 42   Support Vector Machines (SVM)       NaN           0.001   
 43   Support Vector Machines (SVM)       NaN           False   
 44   Support Vector Machines (SVM)  0.387148             NaN   
 45   Support Vector Machines (SVM)  0.062193             NaN   
 46   Support Vector Machines (SVM)  0.812834             NaN   
 47   Support Vector Machines (SVM)  0.115545             NaN   
 48   Support Vector Machines (SVM)  0.646691             NaN   
 49   Support Vector Machines (SVM)  0.245792             NaN   
 50   Support Vector Machines (SVM)  0.293383             NaN   
 51   Support Vector Machines (SVM)       NaN             1.0   
 52   Support Vector Machines (SVM)       NaN           False   
 53   Support Vector Machines (SVM)       NaN             200   
 54   Support Vector Machines (SVM)       NaN            None   
 55   Support Vector Machines (SVM)       NaN             0.0   
 56   Support Vector Machines (SVM)       NaN             ovr   
 57   Support Vector Machines (SVM)       NaN               3   
 58   Support Vector Machines (SVM)       NaN           scale   
 59   Support Vector Machines (SVM)       NaN             rbf   
 60   Support Vector Machines (SVM)       NaN              -1   
 61   Support Vector Machines (SVM)       NaN            True   
 62   Support Vector Machines (SVM)       NaN            None   
 63   Support Vector Machines (SVM)       NaN            True   
 64   Support Vector Machines (SVM)       NaN           0.001   
 65   Support Vector Machines (SVM)       NaN           False   
 66   Support Vector Machines (SVM)  0.376449             NaN   
 67   Support Vector Machines (SVM)  0.065278             NaN   
 68   Support Vector Machines (SVM)  0.831633             NaN   
 69   Support Vector Machines (SVM)  0.121055             NaN   
 70   Support Vector Machines (SVM)  0.638420             NaN   
 71   Support Vector Machines (SVM)  0.236134             NaN   
 72   Support Vector Machines (SVM)  0.276841             NaN   
 73   Support Vector Machines (SVM)       NaN             1.0   
 74   Support Vector Machines (SVM)       NaN           False   
 75   Support Vector Machines (SVM)       NaN             200   
 76   Support Vector Machines (SVM)       NaN            None   
 77   Support Vector Machines (SVM)       NaN             0.0   
 78   Support Vector Machines (SVM)       NaN             ovr   
 79   Support Vector Machines (SVM)       NaN               3   
 80   Support Vector Machines (SVM)       NaN           scale   
 81   Support Vector Machines (SVM)       NaN             rbf   
 82   Support Vector Machines (SVM)       NaN              -1   
 83   Support Vector Machines (SVM)       NaN            True   
 84   Support Vector Machines (SVM)       NaN            None   
 85   Support Vector Machines (SVM)       NaN            True   
 86   Support Vector Machines (SVM)       NaN           0.001   
 87   Support Vector Machines (SVM)       NaN           False   
 88   Support Vector Machines (SVM)  0.406481             NaN   
 89   Support Vector Machines (SVM)  0.072956             NaN   
 90   Support Vector Machines (SVM)  0.805556             NaN   
 91   Support Vector Machines (SVM)  0.133795             NaN   
 92   Support Vector Machines (SVM)  0.666178             NaN   
 93   Support Vector Machines (SVM)  0.267794             NaN   
 94   Support Vector Machines (SVM)  0.332356             NaN   
 95   Support Vector Machines (SVM)       NaN             1.0   
 96   Support Vector Machines (SVM)       NaN           False   
 97   Support Vector Machines (SVM)       NaN             200   
 98   Support Vector Machines (SVM)       NaN            None   
 99   Support Vector Machines (SVM)       NaN             0.0   
 100  Support Vector Machines (SVM)       NaN             ovr   
 101  Support Vector Machines (SVM)       NaN               3   
 102  Support Vector Machines (SVM)       NaN           scale   
 103  Support Vector Machines (SVM)       NaN             rbf   
 104  Support Vector Machines (SVM)       NaN              -1   
 105  Support Vector Machines (SVM)       NaN            True   
 106  Support Vector Machines (SVM)       NaN            None   
 107  Support Vector Machines (SVM)       NaN            True   
 108  Support Vector Machines (SVM)       NaN           0.001   
 109  Support Vector Machines (SVM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0      Median Imputation  Hybrid Sampling (SMOTETomek)   
 1      Median Imputation  Hybrid Sampling (SMOTETomek)   
 2      Median Imputation  Hybrid Sampling (SMOTETomek)   
 3      Median Imputation  Hybrid Sampling (SMOTETomek)   
 4      Median Imputation  Hybrid Sampling (SMOTETomek)   
 5      Median Imputation  Hybrid Sampling (SMOTETomek)   
 6      Median Imputation  Hybrid Sampling (SMOTETomek)   
 7      Median Imputation  Hybrid Sampling (SMOTETomek)   
 8      Median Imputation  Hybrid Sampling (SMOTETomek)   
 9      Median Imputation  Hybrid Sampling (SMOTETomek)   
 10     Median Imputation  Hybrid Sampling (SMOTETomek)   
 11     Median Imputation  Hybrid Sampling (SMOTETomek)   
 12     Median Imputation  Hybrid Sampling (SMOTETomek)   
 13     Median Imputation  Hybrid Sampling (SMOTETomek)   
 14     Median Imputation  Hybrid Sampling (SMOTETomek)   
 15     Median Imputation  Hybrid Sampling (SMOTETomek)   
 16     Median Imputation  Hybrid Sampling (SMOTETomek)   
 17     Median Imputation  Hybrid Sampling (SMOTETomek)   
 18     Median Imputation  Hybrid Sampling (SMOTETomek)   
 19     Median Imputation  Hybrid Sampling (SMOTETomek)   
 20     Median Imputation  Hybrid Sampling (SMOTETomek)   
 21     Median Imputation  Hybrid Sampling (SMOTETomek)   
 22     Median Imputation  Hybrid Sampling (SMOTETomek)   
 23     Median Imputation  Hybrid Sampling (SMOTETomek)   
 24     Median Imputation  Hybrid Sampling (SMOTETomek)   
 25     Median Imputation  Hybrid Sampling (SMOTETomek)   
 26     Median Imputation  Hybrid Sampling (SMOTETomek)   
 27     Median Imputation  Hybrid Sampling (SMOTETomek)   
 28     Median Imputation  Hybrid Sampling (SMOTETomek)   
 29     Median Imputation  Hybrid Sampling (SMOTETomek)   
 30     Median Imputation  Hybrid Sampling (SMOTETomek)   
 31     Median Imputation  Hybrid Sampling (SMOTETomek)   
 32     Median Imputation  Hybrid Sampling (SMOTETomek)   
 33     Median Imputation  Hybrid Sampling (SMOTETomek)   
 34     Median Imputation  Hybrid Sampling (SMOTETomek)   
 35     Median Imputation  Hybrid Sampling (SMOTETomek)   
 36     Median Imputation  Hybrid Sampling (SMOTETomek)   
 37     Median Imputation  Hybrid Sampling (SMOTETomek)   
 38     Median Imputation  Hybrid Sampling (SMOTETomek)   
 39     Median Imputation  Hybrid Sampling (SMOTETomek)   
 40     Median Imputation  Hybrid Sampling (SMOTETomek)   
 41     Median Imputation  Hybrid Sampling (SMOTETomek)   
 42     Median Imputation  Hybrid Sampling (SMOTETomek)   
 43     Median Imputation  Hybrid Sampling (SMOTETomek)   
 44     Median Imputation  Hybrid Sampling (SMOTETomek)   
 45     Median Imputation  Hybrid Sampling (SMOTETomek)   
 46     Median Imputation  Hybrid Sampling (SMOTETomek)   
 47     Median Imputation  Hybrid Sampling (SMOTETomek)   
 48     Median Imputation  Hybrid Sampling (SMOTETomek)   
 49     Median Imputation  Hybrid Sampling (SMOTETomek)   
 50     Median Imputation  Hybrid Sampling (SMOTETomek)   
 51     Median Imputation  Hybrid Sampling (SMOTETomek)   
 52     Median Imputation  Hybrid Sampling (SMOTETomek)   
 53     Median Imputation  Hybrid Sampling (SMOTETomek)   
 54     Median Imputation  Hybrid Sampling (SMOTETomek)   
 55     Median Imputation  Hybrid Sampling (SMOTETomek)   
 56     Median Imputation  Hybrid Sampling (SMOTETomek)   
 57     Median Imputation  Hybrid Sampling (SMOTETomek)   
 58     Median Imputation  Hybrid Sampling (SMOTETomek)   
 59     Median Imputation  Hybrid Sampling (SMOTETomek)   
 60     Median Imputation  Hybrid Sampling (SMOTETomek)   
 61     Median Imputation  Hybrid Sampling (SMOTETomek)   
 62     Median Imputation  Hybrid Sampling (SMOTETomek)   
 63     Median Imputation  Hybrid Sampling (SMOTETomek)   
 64     Median Imputation  Hybrid Sampling (SMOTETomek)   
 65     Median Imputation  Hybrid Sampling (SMOTETomek)   
 66     Median Imputation  Hybrid Sampling (SMOTETomek)   
 67     Median Imputation  Hybrid Sampling (SMOTETomek)   
 68     Median Imputation  Hybrid Sampling (SMOTETomek)   
 69     Median Imputation  Hybrid Sampling (SMOTETomek)   
 70     Median Imputation  Hybrid Sampling (SMOTETomek)   
 71     Median Imputation  Hybrid Sampling (SMOTETomek)   
 72     Median Imputation  Hybrid Sampling (SMOTETomek)   
 73     Median Imputation  Hybrid Sampling (SMOTETomek)   
 74     Median Imputation  Hybrid Sampling (SMOTETomek)   
 75     Median Imputation  Hybrid Sampling (SMOTETomek)   
 76     Median Imputation  Hybrid Sampling (SMOTETomek)   
 77     Median Imputation  Hybrid Sampling (SMOTETomek)   
 78     Median Imputation  Hybrid Sampling (SMOTETomek)   
 79     Median Imputation  Hybrid Sampling (SMOTETomek)   
 80     Median Imputation  Hybrid Sampling (SMOTETomek)   
 81     Median Imputation  Hybrid Sampling (SMOTETomek)   
 82     Median Imputation  Hybrid Sampling (SMOTETomek)   
 83     Median Imputation  Hybrid Sampling (SMOTETomek)   
 84     Median Imputation  Hybrid Sampling (SMOTETomek)   
 85     Median Imputation  Hybrid Sampling (SMOTETomek)   
 86     Median Imputation  Hybrid Sampling (SMOTETomek)   
 87     Median Imputation  Hybrid Sampling (SMOTETomek)   
 88     Median Imputation  Hybrid Sampling (SMOTETomek)   
 89     Median Imputation  Hybrid Sampling (SMOTETomek)   
 90     Median Imputation  Hybrid Sampling (SMOTETomek)   
 91     Median Imputation  Hybrid Sampling (SMOTETomek)   
 92     Median Imputation  Hybrid Sampling (SMOTETomek)   
 93     Median Imputation  Hybrid Sampling (SMOTETomek)   
 94     Median Imputation  Hybrid Sampling (SMOTETomek)   
 95     Median Imputation  Hybrid Sampling (SMOTETomek)   
 96     Median Imputation  Hybrid Sampling (SMOTETomek)   
 97     Median Imputation  Hybrid Sampling (SMOTETomek)   
 98     Median Imputation  Hybrid Sampling (SMOTETomek)   
 99     Median Imputation  Hybrid Sampling (SMOTETomek)   
 100    Median Imputation  Hybrid Sampling (SMOTETomek)   
 101    Median Imputation  Hybrid Sampling (SMOTETomek)   
 102    Median Imputation  Hybrid Sampling (SMOTETomek)   
 103    Median Imputation  Hybrid Sampling (SMOTETomek)   
 104    Median Imputation  Hybrid Sampling (SMOTETomek)   
 105    Median Imputation  Hybrid Sampling (SMOTETomek)   
 106    Median Imputation  Hybrid Sampling (SMOTETomek)   
 107    Median Imputation  Hybrid Sampling (SMOTETomek)   
 108    Median Imputation  Hybrid Sampling (SMOTETomek)   
 109    Median Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Support Vector Machines (SVM)_Hybrid Sampling ...  
 1    Support Vector Machines (SVM)_Hybrid Sampling ...  
 2    Support Vector Machines (SVM)_Hybrid Sampling ...  
 3    Support Vector Machines (SVM)_Hybrid Sampling ...  
 4    Support Vector Machines (SVM)_Hybrid Sampling ...  
 5    Support Vector Machines (SVM)_Hybrid Sampling ...  
 6    Support Vector Machines (SVM)_Hybrid Sampling ...  
 7    Support Vector Machines (SVM)_Hybrid Sampling ...  
 8    Support Vector Machines (SVM)_Hybrid Sampling ...  
 9    Support Vector Machines (SVM)_Hybrid Sampling ...  
 10   Support Vector Machines (SVM)_Hybrid Sampling ...  
 11   Support Vector Machines (SVM)_Hybrid Sampling ...  
 12   Support Vector Machines (SVM)_Hybrid Sampling ...  
 13   Support Vector Machines (SVM)_Hybrid Sampling ...  
 14   Support Vector Machines (SVM)_Hybrid Sampling ...  
 15   Support Vector Machines (SVM)_Hybrid Sampling ...  
 16   Support Vector Machines (SVM)_Hybrid Sampling ...  
 17   Support Vector Machines (SVM)_Hybrid Sampling ...  
 18   Support Vector Machines (SVM)_Hybrid Sampling ...  
 19   Support Vector Machines (SVM)_Hybrid Sampling ...  
 20   Support Vector Machines (SVM)_Hybrid Sampling ...  
 21   Support Vector Machines (SVM)_Hybrid Sampling ...  
 22   Support Vector Machines (SVM)_Hybrid Sampling ...  
 23   Support Vector Machines (SVM)_Hybrid Sampling ...  
 24   Support Vector Machines (SVM)_Hybrid Sampling ...  
 25   Support Vector Machines (SVM)_Hybrid Sampling ...  
 26   Support Vector Machines (SVM)_Hybrid Sampling ...  
 27   Support Vector Machines (SVM)_Hybrid Sampling ...  
 28   Support Vector Machines (SVM)_Hybrid Sampling ...  
 29   Support Vector Machines (SVM)_Hybrid Sampling ...  
 30   Support Vector Machines (SVM)_Hybrid Sampling ...  
 31   Support Vector Machines (SVM)_Hybrid Sampling ...  
 32   Support Vector Machines (SVM)_Hybrid Sampling ...  
 33   Support Vector Machines (SVM)_Hybrid Sampling ...  
 34   Support Vector Machines (SVM)_Hybrid Sampling ...  
 35   Support Vector Machines (SVM)_Hybrid Sampling ...  
 36   Support Vector Machines (SVM)_Hybrid Sampling ...  
 37   Support Vector Machines (SVM)_Hybrid Sampling ...  
 38   Support Vector Machines (SVM)_Hybrid Sampling ...  
 39   Support Vector Machines (SVM)_Hybrid Sampling ...  
 40   Support Vector Machines (SVM)_Hybrid Sampling ...  
 41   Support Vector Machines (SVM)_Hybrid Sampling ...  
 42   Support Vector Machines (SVM)_Hybrid Sampling ...  
 43   Support Vector Machines (SVM)_Hybrid Sampling ...  
 44   Support Vector Machines (SVM)_Hybrid Sampling ...  
 45   Support Vector Machines (SVM)_Hybrid Sampling ...  
 46   Support Vector Machines (SVM)_Hybrid Sampling ...  
 47   Support Vector Machines (SVM)_Hybrid Sampling ...  
 48   Support Vector Machines (SVM)_Hybrid Sampling ...  
 49   Support Vector Machines (SVM)_Hybrid Sampling ...  
 50   Support Vector Machines (SVM)_Hybrid Sampling ...  
 51   Support Vector Machines (SVM)_Hybrid Sampling ...  
 52   Support Vector Machines (SVM)_Hybrid Sampling ...  
 53   Support Vector Machines (SVM)_Hybrid Sampling ...  
 54   Support Vector Machines (SVM)_Hybrid Sampling ...  
 55   Support Vector Machines (SVM)_Hybrid Sampling ...  
 56   Support Vector Machines (SVM)_Hybrid Sampling ...  
 57   Support Vector Machines (SVM)_Hybrid Sampling ...  
 58   Support Vector Machines (SVM)_Hybrid Sampling ...  
 59   Support Vector Machines (SVM)_Hybrid Sampling ...  
 60   Support Vector Machines (SVM)_Hybrid Sampling ...  
 61   Support Vector Machines (SVM)_Hybrid Sampling ...  
 62   Support Vector Machines (SVM)_Hybrid Sampling ...  
 63   Support Vector Machines (SVM)_Hybrid Sampling ...  
 64   Support Vector Machines (SVM)_Hybrid Sampling ...  
 65   Support Vector Machines (SVM)_Hybrid Sampling ...  
 66   Support Vector Machines (SVM)_Hybrid Sampling ...  
 67   Support Vector Machines (SVM)_Hybrid Sampling ...  
 68   Support Vector Machines (SVM)_Hybrid Sampling ...  
 69   Support Vector Machines (SVM)_Hybrid Sampling ...  
 70   Support Vector Machines (SVM)_Hybrid Sampling ...  
 71   Support Vector Machines (SVM)_Hybrid Sampling ...  
 72   Support Vector Machines (SVM)_Hybrid Sampling ...  
 73   Support Vector Machines (SVM)_Hybrid Sampling ...  
 74   Support Vector Machines (SVM)_Hybrid Sampling ...  
 75   Support Vector Machines (SVM)_Hybrid Sampling ...  
 76   Support Vector Machines (SVM)_Hybrid Sampling ...  
 77   Support Vector Machines (SVM)_Hybrid Sampling ...  
 78   Support Vector Machines (SVM)_Hybrid Sampling ...  
 79   Support Vector Machines (SVM)_Hybrid Sampling ...  
 80   Support Vector Machines (SVM)_Hybrid Sampling ...  
 81   Support Vector Machines (SVM)_Hybrid Sampling ...  
 82   Support Vector Machines (SVM)_Hybrid Sampling ...  
 83   Support Vector Machines (SVM)_Hybrid Sampling ...  
 84   Support Vector Machines (SVM)_Hybrid Sampling ...  
 85   Support Vector Machines (SVM)_Hybrid Sampling ...  
 86   Support Vector Machines (SVM)_Hybrid Sampling ...  
 87   Support Vector Machines (SVM)_Hybrid Sampling ...  
 88   Support Vector Machines (SVM)_Hybrid Sampling ...  
 89   Support Vector Machines (SVM)_Hybrid Sampling ...  
 90   Support Vector Machines (SVM)_Hybrid Sampling ...  
 91   Support Vector Machines (SVM)_Hybrid Sampling ...  
 92   Support Vector Machines (SVM)_Hybrid Sampling ...  
 93   Support Vector Machines (SVM)_Hybrid Sampling ...  
 94   Support Vector Machines (SVM)_Hybrid Sampling ...  
 95   Support Vector Machines (SVM)_Hybrid Sampling ...  
 96   Support Vector Machines (SVM)_Hybrid Sampling ...  
 97   Support Vector Machines (SVM)_Hybrid Sampling ...  
 98   Support Vector Machines (SVM)_Hybrid Sampling ...  
 99   Support Vector Machines (SVM)_Hybrid Sampling ...  
 100  Support Vector Machines (SVM)_Hybrid Sampling ...  
 101  Support Vector Machines (SVM)_Hybrid Sampling ...  
 102  Support Vector Machines (SVM)_Hybrid Sampling ...  
 103  Support Vector Machines (SVM)_Hybrid Sampling ...  
 104  Support Vector Machines (SVM)_Hybrid Sampling ...  
 105  Support Vector Machines (SVM)_Hybrid Sampling ...  
 106  Support Vector Machines (SVM)_Hybrid Sampling ...  
 107  Support Vector Machines (SVM)_Hybrid Sampling ...  
 108  Support Vector Machines (SVM)_Hybrid Sampling ...  
 109  Support Vector Machines (SVM)_Hybrid Sampling ...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.674480             NaN      Zero Imputation   
 1    Neural Networks  0.100080             NaN      Zero Imputation   
 2    Neural Networks  0.527426             NaN      Zero Imputation   
 3    Neural Networks  0.168237             NaN      Zero Imputation   
 4    Neural Networks  0.613141             NaN      Zero Imputation   
 5    Neural Networks  0.226182             NaN      Zero Imputation   
 6    Neural Networks  0.226282             NaN      Zero Imputation   
 7    Neural Networks       NaN            relu      Zero Imputation   
 8    Neural Networks       NaN          0.0001      Zero Imputation   
 9    Neural Networks       NaN            auto      Zero Imputation   
 10   Neural Networks       NaN             0.9      Zero Imputation   
 11   Neural Networks       NaN           0.999      Zero Imputation   
 12   Neural Networks       NaN           False      Zero Imputation   
 13   Neural Networks       NaN             0.0      Zero Imputation   
 14   Neural Networks       NaN          (100,)      Zero Imputation   
 15   Neural Networks       NaN        constant      Zero Imputation   
 16   Neural Networks       NaN           0.001      Zero Imputation   
 17   Neural Networks       NaN           15000      Zero Imputation   
 18   Neural Networks       NaN             200      Zero Imputation   
 19   Neural Networks       NaN             0.9      Zero Imputation   
 20   Neural Networks       NaN              10      Zero Imputation   
 21   Neural Networks       NaN            True      Zero Imputation   
 22   Neural Networks       NaN             0.5      Zero Imputation   
 23   Neural Networks       NaN            None      Zero Imputation   
 24   Neural Networks       NaN            True      Zero Imputation   
 25   Neural Networks       NaN            adam      Zero Imputation   
 26   Neural Networks       NaN          0.0001      Zero Imputation   
 27   Neural Networks       NaN             0.1      Zero Imputation   
 28   Neural Networks       NaN           False      Zero Imputation   
 29   Neural Networks       NaN           False      Zero Imputation   
 30   Neural Networks  0.718462             NaN      Zero Imputation   
 31   Neural Networks  0.075117             NaN      Zero Imputation   
 32   Neural Networks  0.487805             NaN      Zero Imputation   
 33   Neural Networks  0.130187             NaN      Zero Imputation   
 34   Neural Networks  0.640110             NaN      Zero Imputation   
 35   Neural Networks  0.246737             NaN      Zero Imputation   
 36   Neural Networks  0.280219             NaN      Zero Imputation   
 37   Neural Networks       NaN            relu      Zero Imputation   
 38   Neural Networks       NaN          0.0001      Zero Imputation   
 39   Neural Networks       NaN            auto      Zero Imputation   
 40   Neural Networks       NaN             0.9      Zero Imputation   
 41   Neural Networks       NaN           0.999      Zero Imputation   
 42   Neural Networks       NaN           False      Zero Imputation   
 43   Neural Networks       NaN             0.0      Zero Imputation   
 44   Neural Networks       NaN          (100,)      Zero Imputation   
 45   Neural Networks       NaN        constant      Zero Imputation   
 46   Neural Networks       NaN           0.001      Zero Imputation   
 47   Neural Networks       NaN           15000      Zero Imputation   
 48   Neural Networks       NaN             200      Zero Imputation   
 49   Neural Networks       NaN             0.9      Zero Imputation   
 50   Neural Networks       NaN              10      Zero Imputation   
 51   Neural Networks       NaN            True      Zero Imputation   
 52   Neural Networks       NaN             0.5      Zero Imputation   
 53   Neural Networks       NaN            None      Zero Imputation   
 54   Neural Networks       NaN            True      Zero Imputation   
 55   Neural Networks       NaN            adam      Zero Imputation   
 56   Neural Networks       NaN          0.0001      Zero Imputation   
 57   Neural Networks       NaN             0.1      Zero Imputation   
 58   Neural Networks       NaN           False      Zero Imputation   
 59   Neural Networks       NaN           False      Zero Imputation   
 60   Neural Networks  0.809323             NaN      Zero Imputation   
 61   Neural Networks  0.105727             NaN      Zero Imputation   
 62   Neural Networks  0.385027             NaN      Zero Imputation   
 63   Neural Networks  0.165899             NaN      Zero Imputation   
 64   Neural Networks  0.636890             NaN      Zero Imputation   
 65   Neural Networks  0.223230             NaN      Zero Imputation   
 66   Neural Networks  0.273779             NaN      Zero Imputation   
 67   Neural Networks       NaN            relu      Zero Imputation   
 68   Neural Networks       NaN          0.0001      Zero Imputation   
 69   Neural Networks       NaN            auto      Zero Imputation   
 70   Neural Networks       NaN             0.9      Zero Imputation   
 71   Neural Networks       NaN           0.999      Zero Imputation   
 72   Neural Networks       NaN           False      Zero Imputation   
 73   Neural Networks       NaN             0.0      Zero Imputation   
 74   Neural Networks       NaN          (100,)      Zero Imputation   
 75   Neural Networks       NaN        constant      Zero Imputation   
 76   Neural Networks       NaN           0.001      Zero Imputation   
 77   Neural Networks       NaN           15000      Zero Imputation   
 78   Neural Networks       NaN             200      Zero Imputation   
 79   Neural Networks       NaN             0.9      Zero Imputation   
 80   Neural Networks       NaN              10      Zero Imputation   
 81   Neural Networks       NaN            True      Zero Imputation   
 82   Neural Networks       NaN             0.5      Zero Imputation   
 83   Neural Networks       NaN            None      Zero Imputation   
 84   Neural Networks       NaN            True      Zero Imputation   
 85   Neural Networks       NaN            adam      Zero Imputation   
 86   Neural Networks       NaN          0.0001      Zero Imputation   
 87   Neural Networks       NaN             0.1      Zero Imputation   
 88   Neural Networks       NaN           False      Zero Imputation   
 89   Neural Networks       NaN           False      Zero Imputation   
 90   Neural Networks  0.756849             NaN      Zero Imputation   
 91   Neural Networks  0.086462             NaN      Zero Imputation   
 92   Neural Networks  0.387755             NaN      Zero Imputation   
 93   Neural Networks  0.141395             NaN      Zero Imputation   
 94   Neural Networks  0.616377             NaN      Zero Imputation   
 95   Neural Networks  0.223611             NaN      Zero Imputation   
 96   Neural Networks  0.232754             NaN      Zero Imputation   
 97   Neural Networks       NaN            relu      Zero Imputation   
 98   Neural Networks       NaN          0.0001      Zero Imputation   
 99   Neural Networks       NaN            auto      Zero Imputation   
 100  Neural Networks       NaN             0.9      Zero Imputation   
 101  Neural Networks       NaN           0.999      Zero Imputation   
 102  Neural Networks       NaN           False      Zero Imputation   
 103  Neural Networks       NaN             0.0      Zero Imputation   
 104  Neural Networks       NaN          (100,)      Zero Imputation   
 105  Neural Networks       NaN        constant      Zero Imputation   
 106  Neural Networks       NaN           0.001      Zero Imputation   
 107  Neural Networks       NaN           15000      Zero Imputation   
 108  Neural Networks       NaN             200      Zero Imputation   
 109  Neural Networks       NaN             0.9      Zero Imputation   
 110  Neural Networks       NaN              10      Zero Imputation   
 111  Neural Networks       NaN            True      Zero Imputation   
 112  Neural Networks       NaN             0.5      Zero Imputation   
 113  Neural Networks       NaN            None      Zero Imputation   
 114  Neural Networks       NaN            True      Zero Imputation   
 115  Neural Networks       NaN            adam      Zero Imputation   
 116  Neural Networks       NaN          0.0001      Zero Imputation   
 117  Neural Networks       NaN             0.1      Zero Imputation   
 118  Neural Networks       NaN           False      Zero Imputation   
 119  Neural Networks       NaN           False      Zero Imputation   
 120  Neural Networks  0.711012             NaN      Zero Imputation   
 121  Neural Networks  0.109832             NaN      Zero Imputation   
 122  Neural Networks  0.574074             NaN      Zero Imputation   
 123  Neural Networks  0.184387             NaN      Zero Imputation   
 124  Neural Networks  0.661795             NaN      Zero Imputation   
 125  Neural Networks  0.311034             NaN      Zero Imputation   
 126  Neural Networks  0.323590             NaN      Zero Imputation   
 127  Neural Networks       NaN            relu      Zero Imputation   
 128  Neural Networks       NaN          0.0001      Zero Imputation   
 129  Neural Networks       NaN            auto      Zero Imputation   
 130  Neural Networks       NaN             0.9      Zero Imputation   
 131  Neural Networks       NaN           0.999      Zero Imputation   
 132  Neural Networks       NaN           False      Zero Imputation   
 133  Neural Networks       NaN             0.0      Zero Imputation   
 134  Neural Networks       NaN          (100,)      Zero Imputation   
 135  Neural Networks       NaN        constant      Zero Imputation   
 136  Neural Networks       NaN           0.001      Zero Imputation   
 137  Neural Networks       NaN           15000      Zero Imputation   
 138  Neural Networks       NaN             200      Zero Imputation   
 139  Neural Networks       NaN             0.9      Zero Imputation   
 140  Neural Networks       NaN              10      Zero Imputation   
 141  Neural Networks       NaN            True      Zero Imputation   
 142  Neural Networks       NaN             0.5      Zero Imputation   
 143  Neural Networks       NaN            None      Zero Imputation   
 144  Neural Networks       NaN            True      Zero Imputation   
 145  Neural Networks       NaN            adam      Zero Imputation   
 146  Neural Networks       NaN          0.0001      Zero Imputation   
 147  Neural Networks       NaN             0.1      Zero Imputation   
 148  Neural Networks       NaN           False      Zero Imputation   
 149  Neural Networks       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 125              Oversampling   
 126              Oversampling   
 127              Oversampling   
 128              Oversampling   
 129              Oversampling   
 130              Oversampling   
 131              Oversampling   
 132              Oversampling   
 133              Oversampling   
 134              Oversampling   
 135              Oversampling   
 136              Oversampling   
 137              Oversampling   
 138              Oversampling   
 139              Oversampling   
 140              Oversampling   
 141              Oversampling   
 142              Oversampling   
 143              Oversampling   
 144              Oversampling   
 145              Oversampling   
 146              Oversampling   
 147              Oversampling   
 148              Oversampling   
 149              Oversampling   
 
                                      Model Unique Code  
 0    Neural Networks_Oversampling_Zero Imputation_f...  
 1    Neural Networks_Oversampling_Zero Imputation_f...  
 2    Neural Networks_Oversampling_Zero Imputation_f...  
 3    Neural Networks_Oversampling_Zero Imputation_f...  
 4    Neural Networks_Oversampling_Zero Imputation_f...  
 5    Neural Networks_Oversampling_Zero Imputation_f...  
 6    Neural Networks_Oversampling_Zero Imputation_f...  
 7    Neural Networks_Oversampling_Zero Imputation_f...  
 8    Neural Networks_Oversampling_Zero Imputation_f...  
 9    Neural Networks_Oversampling_Zero Imputation_f...  
 10   Neural Networks_Oversampling_Zero Imputation_f...  
 11   Neural Networks_Oversampling_Zero Imputation_f...  
 12   Neural Networks_Oversampling_Zero Imputation_f...  
 13   Neural Networks_Oversampling_Zero Imputation_f...  
 14   Neural Networks_Oversampling_Zero Imputation_f...  
 15   Neural Networks_Oversampling_Zero Imputation_f...  
 16   Neural Networks_Oversampling_Zero Imputation_f...  
 17   Neural Networks_Oversampling_Zero Imputation_f...  
 18   Neural Networks_Oversampling_Zero Imputation_f...  
 19   Neural Networks_Oversampling_Zero Imputation_f...  
 20   Neural Networks_Oversampling_Zero Imputation_f...  
 21   Neural Networks_Oversampling_Zero Imputation_f...  
 22   Neural Networks_Oversampling_Zero Imputation_f...  
 23   Neural Networks_Oversampling_Zero Imputation_f...  
 24   Neural Networks_Oversampling_Zero Imputation_f...  
 25   Neural Networks_Oversampling_Zero Imputation_f...  
 26   Neural Networks_Oversampling_Zero Imputation_f...  
 27   Neural Networks_Oversampling_Zero Imputation_f...  
 28   Neural Networks_Oversampling_Zero Imputation_f...  
 29   Neural Networks_Oversampling_Zero Imputation_f...  
 30   Neural Networks_Oversampling_Zero Imputation_f...  
 31   Neural Networks_Oversampling_Zero Imputation_f...  
 32   Neural Networks_Oversampling_Zero Imputation_f...  
 33   Neural Networks_Oversampling_Zero Imputation_f...  
 34   Neural Networks_Oversampling_Zero Imputation_f...  
 35   Neural Networks_Oversampling_Zero Imputation_f...  
 36   Neural Networks_Oversampling_Zero Imputation_f...  
 37   Neural Networks_Oversampling_Zero Imputation_f...  
 38   Neural Networks_Oversampling_Zero Imputation_f...  
 39   Neural Networks_Oversampling_Zero Imputation_f...  
 40   Neural Networks_Oversampling_Zero Imputation_f...  
 41   Neural Networks_Oversampling_Zero Imputation_f...  
 42   Neural Networks_Oversampling_Zero Imputation_f...  
 43   Neural Networks_Oversampling_Zero Imputation_f...  
 44   Neural Networks_Oversampling_Zero Imputation_f...  
 45   Neural Networks_Oversampling_Zero Imputation_f...  
 46   Neural Networks_Oversampling_Zero Imputation_f...  
 47   Neural Networks_Oversampling_Zero Imputation_f...  
 48   Neural Networks_Oversampling_Zero Imputation_f...  
 49   Neural Networks_Oversampling_Zero Imputation_f...  
 50   Neural Networks_Oversampling_Zero Imputation_f...  
 51   Neural Networks_Oversampling_Zero Imputation_f...  
 52   Neural Networks_Oversampling_Zero Imputation_f...  
 53   Neural Networks_Oversampling_Zero Imputation_f...  
 54   Neural Networks_Oversampling_Zero Imputation_f...  
 55   Neural Networks_Oversampling_Zero Imputation_f...  
 56   Neural Networks_Oversampling_Zero Imputation_f...  
 57   Neural Networks_Oversampling_Zero Imputation_f...  
 58   Neural Networks_Oversampling_Zero Imputation_f...  
 59   Neural Networks_Oversampling_Zero Imputation_f...  
 60   Neural Networks_Oversampling_Zero Imputation_f...  
 61   Neural Networks_Oversampling_Zero Imputation_f...  
 62   Neural Networks_Oversampling_Zero Imputation_f...  
 63   Neural Networks_Oversampling_Zero Imputation_f...  
 64   Neural Networks_Oversampling_Zero Imputation_f...  
 65   Neural Networks_Oversampling_Zero Imputation_f...  
 66   Neural Networks_Oversampling_Zero Imputation_f...  
 67   Neural Networks_Oversampling_Zero Imputation_f...  
 68   Neural Networks_Oversampling_Zero Imputation_f...  
 69   Neural Networks_Oversampling_Zero Imputation_f...  
 70   Neural Networks_Oversampling_Zero Imputation_f...  
 71   Neural Networks_Oversampling_Zero Imputation_f...  
 72   Neural Networks_Oversampling_Zero Imputation_f...  
 73   Neural Networks_Oversampling_Zero Imputation_f...  
 74   Neural Networks_Oversampling_Zero Imputation_f...  
 75   Neural Networks_Oversampling_Zero Imputation_f...  
 76   Neural Networks_Oversampling_Zero Imputation_f...  
 77   Neural Networks_Oversampling_Zero Imputation_f...  
 78   Neural Networks_Oversampling_Zero Imputation_f...  
 79   Neural Networks_Oversampling_Zero Imputation_f...  
 80   Neural Networks_Oversampling_Zero Imputation_f...  
 81   Neural Networks_Oversampling_Zero Imputation_f...  
 82   Neural Networks_Oversampling_Zero Imputation_f...  
 83   Neural Networks_Oversampling_Zero Imputation_f...  
 84   Neural Networks_Oversampling_Zero Imputation_f...  
 85   Neural Networks_Oversampling_Zero Imputation_f...  
 86   Neural Networks_Oversampling_Zero Imputation_f...  
 87   Neural Networks_Oversampling_Zero Imputation_f...  
 88   Neural Networks_Oversampling_Zero Imputation_f...  
 89   Neural Networks_Oversampling_Zero Imputation_f...  
 90   Neural Networks_Oversampling_Zero Imputation_f...  
 91   Neural Networks_Oversampling_Zero Imputation_f...  
 92   Neural Networks_Oversampling_Zero Imputation_f...  
 93   Neural Networks_Oversampling_Zero Imputation_f...  
 94   Neural Networks_Oversampling_Zero Imputation_f...  
 95   Neural Networks_Oversampling_Zero Imputation_f...  
 96   Neural Networks_Oversampling_Zero Imputation_f...  
 97   Neural Networks_Oversampling_Zero Imputation_f...  
 98   Neural Networks_Oversampling_Zero Imputation_f...  
 99   Neural Networks_Oversampling_Zero Imputation_f...  
 100  Neural Networks_Oversampling_Zero Imputation_f...  
 101  Neural Networks_Oversampling_Zero Imputation_f...  
 102  Neural Networks_Oversampling_Zero Imputation_f...  
 103  Neural Networks_Oversampling_Zero Imputation_f...  
 104  Neural Networks_Oversampling_Zero Imputation_f...  
 105  Neural Networks_Oversampling_Zero Imputation_f...  
 106  Neural Networks_Oversampling_Zero Imputation_f...  
 107  Neural Networks_Oversampling_Zero Imputation_f...  
 108  Neural Networks_Oversampling_Zero Imputation_f...  
 109  Neural Networks_Oversampling_Zero Imputation_f...  
 110  Neural Networks_Oversampling_Zero Imputation_f...  
 111  Neural Networks_Oversampling_Zero Imputation_f...  
 112  Neural Networks_Oversampling_Zero Imputation_f...  
 113  Neural Networks_Oversampling_Zero Imputation_f...  
 114  Neural Networks_Oversampling_Zero Imputation_f...  
 115  Neural Networks_Oversampling_Zero Imputation_f...  
 116  Neural Networks_Oversampling_Zero Imputation_f...  
 117  Neural Networks_Oversampling_Zero Imputation_f...  
 118  Neural Networks_Oversampling_Zero Imputation_f...  
 119  Neural Networks_Oversampling_Zero Imputation_f...  
 120  Neural Networks_Oversampling_Zero Imputation_f...  
 121  Neural Networks_Oversampling_Zero Imputation_f...  
 122  Neural Networks_Oversampling_Zero Imputation_f...  
 123  Neural Networks_Oversampling_Zero Imputation_f...  
 124  Neural Networks_Oversampling_Zero Imputation_f...  
 125  Neural Networks_Oversampling_Zero Imputation_f...  
 126  Neural Networks_Oversampling_Zero Imputation_f...  
 127  Neural Networks_Oversampling_Zero Imputation_f...  
 128  Neural Networks_Oversampling_Zero Imputation_f...  
 129  Neural Networks_Oversampling_Zero Imputation_f...  
 130  Neural Networks_Oversampling_Zero Imputation_f...  
 131  Neural Networks_Oversampling_Zero Imputation_f...  
 132  Neural Networks_Oversampling_Zero Imputation_f...  
 133  Neural Networks_Oversampling_Zero Imputation_f...  
 134  Neural Networks_Oversampling_Zero Imputation_f...  
 135  Neural Networks_Oversampling_Zero Imputation_f...  
 136  Neural Networks_Oversampling_Zero Imputation_f...  
 137  Neural Networks_Oversampling_Zero Imputation_f...  
 138  Neural Networks_Oversampling_Zero Imputation_f...  
 139  Neural Networks_Oversampling_Zero Imputation_f...  
 140  Neural Networks_Oversampling_Zero Imputation_f...  
 141  Neural Networks_Oversampling_Zero Imputation_f...  
 142  Neural Networks_Oversampling_Zero Imputation_f...  
 143  Neural Networks_Oversampling_Zero Imputation_f...  
 144  Neural Networks_Oversampling_Zero Imputation_f...  
 145  Neural Networks_Oversampling_Zero Imputation_f...  
 146  Neural Networks_Oversampling_Zero Imputation_f...  
 147  Neural Networks_Oversampling_Zero Imputation_f...  
 148  Neural Networks_Oversampling_Zero Imputation_f...  
 149  Neural Networks_Oversampling_Zero Imputation_f...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.580985             NaN      Mean Imputation   
 1    Neural Networks  0.077403             NaN      Mean Imputation   
 2    Neural Networks  0.523207             NaN      Mean Imputation   
 3    Neural Networks  0.134856             NaN      Mean Imputation   
 4    Neural Networks  0.579217             NaN      Mean Imputation   
 5    Neural Networks  0.147069             NaN      Mean Imputation   
 6    Neural Networks  0.158434             NaN      Mean Imputation   
 7    Neural Networks       NaN            relu      Mean Imputation   
 8    Neural Networks       NaN          0.0001      Mean Imputation   
 9    Neural Networks       NaN            auto      Mean Imputation   
 10   Neural Networks       NaN             0.9      Mean Imputation   
 11   Neural Networks       NaN           0.999      Mean Imputation   
 12   Neural Networks       NaN           False      Mean Imputation   
 13   Neural Networks       NaN             0.0      Mean Imputation   
 14   Neural Networks       NaN          (100,)      Mean Imputation   
 15   Neural Networks       NaN        constant      Mean Imputation   
 16   Neural Networks       NaN           0.001      Mean Imputation   
 17   Neural Networks       NaN           15000      Mean Imputation   
 18   Neural Networks       NaN             200      Mean Imputation   
 19   Neural Networks       NaN             0.9      Mean Imputation   
 20   Neural Networks       NaN              10      Mean Imputation   
 21   Neural Networks       NaN            True      Mean Imputation   
 22   Neural Networks       NaN             0.5      Mean Imputation   
 23   Neural Networks       NaN            None      Mean Imputation   
 24   Neural Networks       NaN            True      Mean Imputation   
 25   Neural Networks       NaN            adam      Mean Imputation   
 26   Neural Networks       NaN          0.0001      Mean Imputation   
 27   Neural Networks       NaN             0.1      Mean Imputation   
 28   Neural Networks       NaN           False      Mean Imputation   
 29   Neural Networks       NaN           False      Mean Imputation   
 30   Neural Networks  0.921254             NaN      Mean Imputation   
 31   Neural Networks  0.090909             NaN      Mean Imputation   
 32   Neural Networks  0.091463             NaN      Mean Imputation   
 33   Neural Networks  0.091185             NaN      Mean Imputation   
 34   Neural Networks  0.651277             NaN      Mean Imputation   
 35   Neural Networks  0.287792             NaN      Mean Imputation   
 36   Neural Networks  0.302553             NaN      Mean Imputation   
 37   Neural Networks       NaN            relu      Mean Imputation   
 38   Neural Networks       NaN          0.0001      Mean Imputation   
 39   Neural Networks       NaN            auto      Mean Imputation   
 40   Neural Networks       NaN             0.9      Mean Imputation   
 41   Neural Networks       NaN           0.999      Mean Imputation   
 42   Neural Networks       NaN           False      Mean Imputation   
 43   Neural Networks       NaN             0.0      Mean Imputation   
 44   Neural Networks       NaN          (100,)      Mean Imputation   
 45   Neural Networks       NaN        constant      Mean Imputation   
 46   Neural Networks       NaN           0.001      Mean Imputation   
 47   Neural Networks       NaN           15000      Mean Imputation   
 48   Neural Networks       NaN             200      Mean Imputation   
 49   Neural Networks       NaN             0.9      Mean Imputation   
 50   Neural Networks       NaN              10      Mean Imputation   
 51   Neural Networks       NaN            True      Mean Imputation   
 52   Neural Networks       NaN             0.5      Mean Imputation   
 53   Neural Networks       NaN            None      Mean Imputation   
 54   Neural Networks       NaN            True      Mean Imputation   
 55   Neural Networks       NaN            adam      Mean Imputation   
 56   Neural Networks       NaN          0.0001      Mean Imputation   
 57   Neural Networks       NaN             0.1      Mean Imputation   
 58   Neural Networks       NaN           False      Mean Imputation   
 59   Neural Networks       NaN           False      Mean Imputation   
 60   Neural Networks  0.648670             NaN      Mean Imputation   
 61   Neural Networks  0.074870             NaN      Mean Imputation   
 62   Neural Networks  0.540107             NaN      Mean Imputation   
 63   Neural Networks  0.131510             NaN      Mean Imputation   
 64   Neural Networks  0.635684             NaN      Mean Imputation   
 65   Neural Networks  0.230736             NaN      Mean Imputation   
 66   Neural Networks  0.271369             NaN      Mean Imputation   
 67   Neural Networks       NaN            relu      Mean Imputation   
 68   Neural Networks       NaN          0.0001      Mean Imputation   
 69   Neural Networks       NaN            auto      Mean Imputation   
 70   Neural Networks       NaN             0.9      Mean Imputation   
 71   Neural Networks       NaN           0.999      Mean Imputation   
 72   Neural Networks       NaN           False      Mean Imputation   
 73   Neural Networks       NaN             0.0      Mean Imputation   
 74   Neural Networks       NaN          (100,)      Mean Imputation   
 75   Neural Networks       NaN        constant      Mean Imputation   
 76   Neural Networks       NaN           0.001      Mean Imputation   
 77   Neural Networks       NaN           15000      Mean Imputation   
 78   Neural Networks       NaN             200      Mean Imputation   
 79   Neural Networks       NaN             0.9      Mean Imputation   
 80   Neural Networks       NaN              10      Mean Imputation   
 81   Neural Networks       NaN            True      Mean Imputation   
 82   Neural Networks       NaN             0.5      Mean Imputation   
 83   Neural Networks       NaN            None      Mean Imputation   
 84   Neural Networks       NaN            True      Mean Imputation   
 85   Neural Networks       NaN            adam      Mean Imputation   
 86   Neural Networks       NaN          0.0001      Mean Imputation   
 87   Neural Networks       NaN             0.1      Mean Imputation   
 88   Neural Networks       NaN           False      Mean Imputation   
 89   Neural Networks       NaN           False      Mean Imputation   
 90   Neural Networks  0.920969             NaN      Mean Imputation   
 91   Neural Networks  0.106061             NaN      Mean Imputation   
 92   Neural Networks  0.071429             NaN      Mean Imputation   
 93   Neural Networks  0.085366             NaN      Mean Imputation   
 94   Neural Networks  0.643456             NaN      Mean Imputation   
 95   Neural Networks  0.270765             NaN      Mean Imputation   
 96   Neural Networks  0.286912             NaN      Mean Imputation   
 97   Neural Networks       NaN            relu      Mean Imputation   
 98   Neural Networks       NaN          0.0001      Mean Imputation   
 99   Neural Networks       NaN            auto      Mean Imputation   
 100  Neural Networks       NaN             0.9      Mean Imputation   
 101  Neural Networks       NaN           0.999      Mean Imputation   
 102  Neural Networks       NaN           False      Mean Imputation   
 103  Neural Networks       NaN             0.0      Mean Imputation   
 104  Neural Networks       NaN          (100,)      Mean Imputation   
 105  Neural Networks       NaN        constant      Mean Imputation   
 106  Neural Networks       NaN           0.001      Mean Imputation   
 107  Neural Networks       NaN           15000      Mean Imputation   
 108  Neural Networks       NaN             200      Mean Imputation   
 109  Neural Networks       NaN             0.9      Mean Imputation   
 110  Neural Networks       NaN              10      Mean Imputation   
 111  Neural Networks       NaN            True      Mean Imputation   
 112  Neural Networks       NaN             0.5      Mean Imputation   
 113  Neural Networks       NaN            None      Mean Imputation   
 114  Neural Networks       NaN            True      Mean Imputation   
 115  Neural Networks       NaN            adam      Mean Imputation   
 116  Neural Networks       NaN          0.0001      Mean Imputation   
 117  Neural Networks       NaN             0.1      Mean Imputation   
 118  Neural Networks       NaN           False      Mean Imputation   
 119  Neural Networks       NaN           False      Mean Imputation   
 120  Neural Networks  0.640674             NaN      Mean Imputation   
 121  Neural Networks  0.087644             NaN      Mean Imputation   
 122  Neural Networks  0.564815             NaN      Mean Imputation   
 123  Neural Networks  0.151741             NaN      Mean Imputation   
 124  Neural Networks  0.648949             NaN      Mean Imputation   
 125  Neural Networks  0.233240             NaN      Mean Imputation   
 126  Neural Networks  0.297899             NaN      Mean Imputation   
 127  Neural Networks       NaN            relu      Mean Imputation   
 128  Neural Networks       NaN          0.0001      Mean Imputation   
 129  Neural Networks       NaN            auto      Mean Imputation   
 130  Neural Networks       NaN             0.9      Mean Imputation   
 131  Neural Networks       NaN           0.999      Mean Imputation   
 132  Neural Networks       NaN           False      Mean Imputation   
 133  Neural Networks       NaN             0.0      Mean Imputation   
 134  Neural Networks       NaN          (100,)      Mean Imputation   
 135  Neural Networks       NaN        constant      Mean Imputation   
 136  Neural Networks       NaN           0.001      Mean Imputation   
 137  Neural Networks       NaN           15000      Mean Imputation   
 138  Neural Networks       NaN             200      Mean Imputation   
 139  Neural Networks       NaN             0.9      Mean Imputation   
 140  Neural Networks       NaN              10      Mean Imputation   
 141  Neural Networks       NaN            True      Mean Imputation   
 142  Neural Networks       NaN             0.5      Mean Imputation   
 143  Neural Networks       NaN            None      Mean Imputation   
 144  Neural Networks       NaN            True      Mean Imputation   
 145  Neural Networks       NaN            adam      Mean Imputation   
 146  Neural Networks       NaN          0.0001      Mean Imputation   
 147  Neural Networks       NaN             0.1      Mean Imputation   
 148  Neural Networks       NaN           False      Mean Imputation   
 149  Neural Networks       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 125              Oversampling   
 126              Oversampling   
 127              Oversampling   
 128              Oversampling   
 129              Oversampling   
 130              Oversampling   
 131              Oversampling   
 132              Oversampling   
 133              Oversampling   
 134              Oversampling   
 135              Oversampling   
 136              Oversampling   
 137              Oversampling   
 138              Oversampling   
 139              Oversampling   
 140              Oversampling   
 141              Oversampling   
 142              Oversampling   
 143              Oversampling   
 144              Oversampling   
 145              Oversampling   
 146              Oversampling   
 147              Oversampling   
 148              Oversampling   
 149              Oversampling   
 
                                      Model Unique Code  
 0    Neural Networks_Oversampling_Mean Imputation_f...  
 1    Neural Networks_Oversampling_Mean Imputation_f...  
 2    Neural Networks_Oversampling_Mean Imputation_f...  
 3    Neural Networks_Oversampling_Mean Imputation_f...  
 4    Neural Networks_Oversampling_Mean Imputation_f...  
 5    Neural Networks_Oversampling_Mean Imputation_f...  
 6    Neural Networks_Oversampling_Mean Imputation_f...  
 7    Neural Networks_Oversampling_Mean Imputation_f...  
 8    Neural Networks_Oversampling_Mean Imputation_f...  
 9    Neural Networks_Oversampling_Mean Imputation_f...  
 10   Neural Networks_Oversampling_Mean Imputation_f...  
 11   Neural Networks_Oversampling_Mean Imputation_f...  
 12   Neural Networks_Oversampling_Mean Imputation_f...  
 13   Neural Networks_Oversampling_Mean Imputation_f...  
 14   Neural Networks_Oversampling_Mean Imputation_f...  
 15   Neural Networks_Oversampling_Mean Imputation_f...  
 16   Neural Networks_Oversampling_Mean Imputation_f...  
 17   Neural Networks_Oversampling_Mean Imputation_f...  
 18   Neural Networks_Oversampling_Mean Imputation_f...  
 19   Neural Networks_Oversampling_Mean Imputation_f...  
 20   Neural Networks_Oversampling_Mean Imputation_f...  
 21   Neural Networks_Oversampling_Mean Imputation_f...  
 22   Neural Networks_Oversampling_Mean Imputation_f...  
 23   Neural Networks_Oversampling_Mean Imputation_f...  
 24   Neural Networks_Oversampling_Mean Imputation_f...  
 25   Neural Networks_Oversampling_Mean Imputation_f...  
 26   Neural Networks_Oversampling_Mean Imputation_f...  
 27   Neural Networks_Oversampling_Mean Imputation_f...  
 28   Neural Networks_Oversampling_Mean Imputation_f...  
 29   Neural Networks_Oversampling_Mean Imputation_f...  
 30   Neural Networks_Oversampling_Mean Imputation_f...  
 31   Neural Networks_Oversampling_Mean Imputation_f...  
 32   Neural Networks_Oversampling_Mean Imputation_f...  
 33   Neural Networks_Oversampling_Mean Imputation_f...  
 34   Neural Networks_Oversampling_Mean Imputation_f...  
 35   Neural Networks_Oversampling_Mean Imputation_f...  
 36   Neural Networks_Oversampling_Mean Imputation_f...  
 37   Neural Networks_Oversampling_Mean Imputation_f...  
 38   Neural Networks_Oversampling_Mean Imputation_f...  
 39   Neural Networks_Oversampling_Mean Imputation_f...  
 40   Neural Networks_Oversampling_Mean Imputation_f...  
 41   Neural Networks_Oversampling_Mean Imputation_f...  
 42   Neural Networks_Oversampling_Mean Imputation_f...  
 43   Neural Networks_Oversampling_Mean Imputation_f...  
 44   Neural Networks_Oversampling_Mean Imputation_f...  
 45   Neural Networks_Oversampling_Mean Imputation_f...  
 46   Neural Networks_Oversampling_Mean Imputation_f...  
 47   Neural Networks_Oversampling_Mean Imputation_f...  
 48   Neural Networks_Oversampling_Mean Imputation_f...  
 49   Neural Networks_Oversampling_Mean Imputation_f...  
 50   Neural Networks_Oversampling_Mean Imputation_f...  
 51   Neural Networks_Oversampling_Mean Imputation_f...  
 52   Neural Networks_Oversampling_Mean Imputation_f...  
 53   Neural Networks_Oversampling_Mean Imputation_f...  
 54   Neural Networks_Oversampling_Mean Imputation_f...  
 55   Neural Networks_Oversampling_Mean Imputation_f...  
 56   Neural Networks_Oversampling_Mean Imputation_f...  
 57   Neural Networks_Oversampling_Mean Imputation_f...  
 58   Neural Networks_Oversampling_Mean Imputation_f...  
 59   Neural Networks_Oversampling_Mean Imputation_f...  
 60   Neural Networks_Oversampling_Mean Imputation_f...  
 61   Neural Networks_Oversampling_Mean Imputation_f...  
 62   Neural Networks_Oversampling_Mean Imputation_f...  
 63   Neural Networks_Oversampling_Mean Imputation_f...  
 64   Neural Networks_Oversampling_Mean Imputation_f...  
 65   Neural Networks_Oversampling_Mean Imputation_f...  
 66   Neural Networks_Oversampling_Mean Imputation_f...  
 67   Neural Networks_Oversampling_Mean Imputation_f...  
 68   Neural Networks_Oversampling_Mean Imputation_f...  
 69   Neural Networks_Oversampling_Mean Imputation_f...  
 70   Neural Networks_Oversampling_Mean Imputation_f...  
 71   Neural Networks_Oversampling_Mean Imputation_f...  
 72   Neural Networks_Oversampling_Mean Imputation_f...  
 73   Neural Networks_Oversampling_Mean Imputation_f...  
 74   Neural Networks_Oversampling_Mean Imputation_f...  
 75   Neural Networks_Oversampling_Mean Imputation_f...  
 76   Neural Networks_Oversampling_Mean Imputation_f...  
 77   Neural Networks_Oversampling_Mean Imputation_f...  
 78   Neural Networks_Oversampling_Mean Imputation_f...  
 79   Neural Networks_Oversampling_Mean Imputation_f...  
 80   Neural Networks_Oversampling_Mean Imputation_f...  
 81   Neural Networks_Oversampling_Mean Imputation_f...  
 82   Neural Networks_Oversampling_Mean Imputation_f...  
 83   Neural Networks_Oversampling_Mean Imputation_f...  
 84   Neural Networks_Oversampling_Mean Imputation_f...  
 85   Neural Networks_Oversampling_Mean Imputation_f...  
 86   Neural Networks_Oversampling_Mean Imputation_f...  
 87   Neural Networks_Oversampling_Mean Imputation_f...  
 88   Neural Networks_Oversampling_Mean Imputation_f...  
 89   Neural Networks_Oversampling_Mean Imputation_f...  
 90   Neural Networks_Oversampling_Mean Imputation_f...  
 91   Neural Networks_Oversampling_Mean Imputation_f...  
 92   Neural Networks_Oversampling_Mean Imputation_f...  
 93   Neural Networks_Oversampling_Mean Imputation_f...  
 94   Neural Networks_Oversampling_Mean Imputation_f...  
 95   Neural Networks_Oversampling_Mean Imputation_f...  
 96   Neural Networks_Oversampling_Mean Imputation_f...  
 97   Neural Networks_Oversampling_Mean Imputation_f...  
 98   Neural Networks_Oversampling_Mean Imputation_f...  
 99   Neural Networks_Oversampling_Mean Imputation_f...  
 100  Neural Networks_Oversampling_Mean Imputation_f...  
 101  Neural Networks_Oversampling_Mean Imputation_f...  
 102  Neural Networks_Oversampling_Mean Imputation_f...  
 103  Neural Networks_Oversampling_Mean Imputation_f...  
 104  Neural Networks_Oversampling_Mean Imputation_f...  
 105  Neural Networks_Oversampling_Mean Imputation_f...  
 106  Neural Networks_Oversampling_Mean Imputation_f...  
 107  Neural Networks_Oversampling_Mean Imputation_f...  
 108  Neural Networks_Oversampling_Mean Imputation_f...  
 109  Neural Networks_Oversampling_Mean Imputation_f...  
 110  Neural Networks_Oversampling_Mean Imputation_f...  
 111  Neural Networks_Oversampling_Mean Imputation_f...  
 112  Neural Networks_Oversampling_Mean Imputation_f...  
 113  Neural Networks_Oversampling_Mean Imputation_f...  
 114  Neural Networks_Oversampling_Mean Imputation_f...  
 115  Neural Networks_Oversampling_Mean Imputation_f...  
 116  Neural Networks_Oversampling_Mean Imputation_f...  
 117  Neural Networks_Oversampling_Mean Imputation_f...  
 118  Neural Networks_Oversampling_Mean Imputation_f...  
 119  Neural Networks_Oversampling_Mean Imputation_f...  
 120  Neural Networks_Oversampling_Mean Imputation_f...  
 121  Neural Networks_Oversampling_Mean Imputation_f...  
 122  Neural Networks_Oversampling_Mean Imputation_f...  
 123  Neural Networks_Oversampling_Mean Imputation_f...  
 124  Neural Networks_Oversampling_Mean Imputation_f...  
 125  Neural Networks_Oversampling_Mean Imputation_f...  
 126  Neural Networks_Oversampling_Mean Imputation_f...  
 127  Neural Networks_Oversampling_Mean Imputation_f...  
 128  Neural Networks_Oversampling_Mean Imputation_f...  
 129  Neural Networks_Oversampling_Mean Imputation_f...  
 130  Neural Networks_Oversampling_Mean Imputation_f...  
 131  Neural Networks_Oversampling_Mean Imputation_f...  
 132  Neural Networks_Oversampling_Mean Imputation_f...  
 133  Neural Networks_Oversampling_Mean Imputation_f...  
 134  Neural Networks_Oversampling_Mean Imputation_f...  
 135  Neural Networks_Oversampling_Mean Imputation_f...  
 136  Neural Networks_Oversampling_Mean Imputation_f...  
 137  Neural Networks_Oversampling_Mean Imputation_f...  
 138  Neural Networks_Oversampling_Mean Imputation_f...  
 139  Neural Networks_Oversampling_Mean Imputation_f...  
 140  Neural Networks_Oversampling_Mean Imputation_f...  
 141  Neural Networks_Oversampling_Mean Imputation_f...  
 142  Neural Networks_Oversampling_Mean Imputation_f...  
 143  Neural Networks_Oversampling_Mean Imputation_f...  
 144  Neural Networks_Oversampling_Mean Imputation_f...  
 145  Neural Networks_Oversampling_Mean Imputation_f...  
 146  Neural Networks_Oversampling_Mean Imputation_f...  
 147  Neural Networks_Oversampling_Mean Imputation_f...  
 148  Neural Networks_Oversampling_Mean Imputation_f...  
 149  Neural Networks_Oversampling_Mean Imputation_f...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.699763             NaN    Median Imputation   
 1    Neural Networks  0.095792             NaN    Median Imputation   
 2    Neural Networks  0.451477             NaN    Median Imputation   
 3    Neural Networks  0.158050             NaN    Median Imputation   
 4    Neural Networks  0.636304             NaN    Median Imputation   
 5    Neural Networks  0.214517             NaN    Median Imputation   
 6    Neural Networks  0.272608             NaN    Median Imputation   
 7    Neural Networks       NaN            relu    Median Imputation   
 8    Neural Networks       NaN          0.0001    Median Imputation   
 9    Neural Networks       NaN            auto    Median Imputation   
 10   Neural Networks       NaN             0.9    Median Imputation   
 11   Neural Networks       NaN           0.999    Median Imputation   
 12   Neural Networks       NaN           False    Median Imputation   
 13   Neural Networks       NaN             0.0    Median Imputation   
 14   Neural Networks       NaN          (100,)    Median Imputation   
 15   Neural Networks       NaN        constant    Median Imputation   
 16   Neural Networks       NaN           0.001    Median Imputation   
 17   Neural Networks       NaN           15000    Median Imputation   
 18   Neural Networks       NaN             200    Median Imputation   
 19   Neural Networks       NaN             0.9    Median Imputation   
 20   Neural Networks       NaN              10    Median Imputation   
 21   Neural Networks       NaN            True    Median Imputation   
 22   Neural Networks       NaN             0.5    Median Imputation   
 23   Neural Networks       NaN            None    Median Imputation   
 24   Neural Networks       NaN            True    Median Imputation   
 25   Neural Networks       NaN            adam    Median Imputation   
 26   Neural Networks       NaN          0.0001    Median Imputation   
 27   Neural Networks       NaN             0.1    Median Imputation   
 28   Neural Networks       NaN           False    Median Imputation   
 29   Neural Networks       NaN           False    Median Imputation   
 30   Neural Networks  0.608902             NaN    Median Imputation   
 31   Neural Networks  0.065174             NaN    Median Imputation   
 32   Neural Networks  0.603659             NaN    Median Imputation   
 33   Neural Networks  0.117647             NaN    Median Imputation   
 34   Neural Networks  0.622682             NaN    Median Imputation   
 35   Neural Networks  0.223233             NaN    Median Imputation   
 36   Neural Networks  0.245364             NaN    Median Imputation   
 37   Neural Networks       NaN            relu    Median Imputation   
 38   Neural Networks       NaN          0.0001    Median Imputation   
 39   Neural Networks       NaN            auto    Median Imputation   
 40   Neural Networks       NaN             0.9    Median Imputation   
 41   Neural Networks       NaN           0.999    Median Imputation   
 42   Neural Networks       NaN           False    Median Imputation   
 43   Neural Networks       NaN             0.0    Median Imputation   
 44   Neural Networks       NaN          (100,)    Median Imputation   
 45   Neural Networks       NaN        constant    Median Imputation   
 46   Neural Networks       NaN           0.001    Median Imputation   
 47   Neural Networks       NaN           15000    Median Imputation   
 48   Neural Networks       NaN             200    Median Imputation   
 49   Neural Networks       NaN             0.9    Median Imputation   
 50   Neural Networks       NaN              10    Median Imputation   
 51   Neural Networks       NaN            True    Median Imputation   
 52   Neural Networks       NaN             0.5    Median Imputation   
 53   Neural Networks       NaN            None    Median Imputation   
 54   Neural Networks       NaN            True    Median Imputation   
 55   Neural Networks       NaN            adam    Median Imputation   
 56   Neural Networks       NaN          0.0001    Median Imputation   
 57   Neural Networks       NaN             0.1    Median Imputation   
 58   Neural Networks       NaN           False    Median Imputation   
 59   Neural Networks       NaN           False    Median Imputation   
 60   Neural Networks  0.595207             NaN    Median Imputation   
 61   Neural Networks  0.070611             NaN    Median Imputation   
 62   Neural Networks  0.593583             NaN    Median Imputation   
 63   Neural Networks  0.126208             NaN    Median Imputation   
 64   Neural Networks  0.640814             NaN    Median Imputation   
 65   Neural Networks  0.218094             NaN    Median Imputation   
 66   Neural Networks  0.281627             NaN    Median Imputation   
 67   Neural Networks       NaN            relu    Median Imputation   
 68   Neural Networks       NaN          0.0001    Median Imputation   
 69   Neural Networks       NaN            auto    Median Imputation   
 70   Neural Networks       NaN             0.9    Median Imputation   
 71   Neural Networks       NaN           0.999    Median Imputation   
 72   Neural Networks       NaN           False    Median Imputation   
 73   Neural Networks       NaN             0.0    Median Imputation   
 74   Neural Networks       NaN          (100,)    Median Imputation   
 75   Neural Networks       NaN        constant    Median Imputation   
 76   Neural Networks       NaN           0.001    Median Imputation   
 77   Neural Networks       NaN           15000    Median Imputation   
 78   Neural Networks       NaN             200    Median Imputation   
 79   Neural Networks       NaN             0.9    Median Imputation   
 80   Neural Networks       NaN              10    Median Imputation   
 81   Neural Networks       NaN            True    Median Imputation   
 82   Neural Networks       NaN             0.5    Median Imputation   
 83   Neural Networks       NaN            None    Median Imputation   
 84   Neural Networks       NaN            True    Median Imputation   
 85   Neural Networks       NaN            adam    Median Imputation   
 86   Neural Networks       NaN          0.0001    Median Imputation   
 87   Neural Networks       NaN             0.1    Median Imputation   
 88   Neural Networks       NaN           False    Median Imputation   
 89   Neural Networks       NaN           False    Median Imputation   
 90   Neural Networks  0.896997             NaN    Median Imputation   
 91   Neural Networks  0.108434             NaN    Median Imputation   
 92   Neural Networks  0.137755             NaN    Median Imputation   
 93   Neural Networks  0.121348             NaN    Median Imputation   
 94   Neural Networks  0.638045             NaN    Median Imputation   
 95   Neural Networks  0.236973             NaN    Median Imputation   
 96   Neural Networks  0.276090             NaN    Median Imputation   
 97   Neural Networks       NaN            relu    Median Imputation   
 98   Neural Networks       NaN          0.0001    Median Imputation   
 99   Neural Networks       NaN            auto    Median Imputation   
 100  Neural Networks       NaN             0.9    Median Imputation   
 101  Neural Networks       NaN           0.999    Median Imputation   
 102  Neural Networks       NaN           False    Median Imputation   
 103  Neural Networks       NaN             0.0    Median Imputation   
 104  Neural Networks       NaN          (100,)    Median Imputation   
 105  Neural Networks       NaN        constant    Median Imputation   
 106  Neural Networks       NaN           0.001    Median Imputation   
 107  Neural Networks       NaN           15000    Median Imputation   
 108  Neural Networks       NaN             200    Median Imputation   
 109  Neural Networks       NaN             0.9    Median Imputation   
 110  Neural Networks       NaN              10    Median Imputation   
 111  Neural Networks       NaN            True    Median Imputation   
 112  Neural Networks       NaN             0.5    Median Imputation   
 113  Neural Networks       NaN            None    Median Imputation   
 114  Neural Networks       NaN            True    Median Imputation   
 115  Neural Networks       NaN            adam    Median Imputation   
 116  Neural Networks       NaN          0.0001    Median Imputation   
 117  Neural Networks       NaN             0.1    Median Imputation   
 118  Neural Networks       NaN           False    Median Imputation   
 119  Neural Networks       NaN           False    Median Imputation   
 120  Neural Networks  0.904110             NaN    Median Imputation   
 121  Neural Networks  0.084270             NaN    Median Imputation   
 122  Neural Networks  0.069444             NaN    Median Imputation   
 123  Neural Networks  0.076142             NaN    Median Imputation   
 124  Neural Networks  0.666552             NaN    Median Imputation   
 125  Neural Networks  0.313377             NaN    Median Imputation   
 126  Neural Networks  0.333104             NaN    Median Imputation   
 127  Neural Networks       NaN            relu    Median Imputation   
 128  Neural Networks       NaN          0.0001    Median Imputation   
 129  Neural Networks       NaN            auto    Median Imputation   
 130  Neural Networks       NaN             0.9    Median Imputation   
 131  Neural Networks       NaN           0.999    Median Imputation   
 132  Neural Networks       NaN           False    Median Imputation   
 133  Neural Networks       NaN             0.0    Median Imputation   
 134  Neural Networks       NaN          (100,)    Median Imputation   
 135  Neural Networks       NaN        constant    Median Imputation   
 136  Neural Networks       NaN           0.001    Median Imputation   
 137  Neural Networks       NaN           15000    Median Imputation   
 138  Neural Networks       NaN             200    Median Imputation   
 139  Neural Networks       NaN             0.9    Median Imputation   
 140  Neural Networks       NaN              10    Median Imputation   
 141  Neural Networks       NaN            True    Median Imputation   
 142  Neural Networks       NaN             0.5    Median Imputation   
 143  Neural Networks       NaN            None    Median Imputation   
 144  Neural Networks       NaN            True    Median Imputation   
 145  Neural Networks       NaN            adam    Median Imputation   
 146  Neural Networks       NaN          0.0001    Median Imputation   
 147  Neural Networks       NaN             0.1    Median Imputation   
 148  Neural Networks       NaN           False    Median Imputation   
 149  Neural Networks       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 125              Oversampling   
 126              Oversampling   
 127              Oversampling   
 128              Oversampling   
 129              Oversampling   
 130              Oversampling   
 131              Oversampling   
 132              Oversampling   
 133              Oversampling   
 134              Oversampling   
 135              Oversampling   
 136              Oversampling   
 137              Oversampling   
 138              Oversampling   
 139              Oversampling   
 140              Oversampling   
 141              Oversampling   
 142              Oversampling   
 143              Oversampling   
 144              Oversampling   
 145              Oversampling   
 146              Oversampling   
 147              Oversampling   
 148              Oversampling   
 149              Oversampling   
 
                                      Model Unique Code  
 0    Neural Networks_Oversampling_Median Imputation...  
 1    Neural Networks_Oversampling_Median Imputation...  
 2    Neural Networks_Oversampling_Median Imputation...  
 3    Neural Networks_Oversampling_Median Imputation...  
 4    Neural Networks_Oversampling_Median Imputation...  
 5    Neural Networks_Oversampling_Median Imputation...  
 6    Neural Networks_Oversampling_Median Imputation...  
 7    Neural Networks_Oversampling_Median Imputation...  
 8    Neural Networks_Oversampling_Median Imputation...  
 9    Neural Networks_Oversampling_Median Imputation...  
 10   Neural Networks_Oversampling_Median Imputation...  
 11   Neural Networks_Oversampling_Median Imputation...  
 12   Neural Networks_Oversampling_Median Imputation...  
 13   Neural Networks_Oversampling_Median Imputation...  
 14   Neural Networks_Oversampling_Median Imputation...  
 15   Neural Networks_Oversampling_Median Imputation...  
 16   Neural Networks_Oversampling_Median Imputation...  
 17   Neural Networks_Oversampling_Median Imputation...  
 18   Neural Networks_Oversampling_Median Imputation...  
 19   Neural Networks_Oversampling_Median Imputation...  
 20   Neural Networks_Oversampling_Median Imputation...  
 21   Neural Networks_Oversampling_Median Imputation...  
 22   Neural Networks_Oversampling_Median Imputation...  
 23   Neural Networks_Oversampling_Median Imputation...  
 24   Neural Networks_Oversampling_Median Imputation...  
 25   Neural Networks_Oversampling_Median Imputation...  
 26   Neural Networks_Oversampling_Median Imputation...  
 27   Neural Networks_Oversampling_Median Imputation...  
 28   Neural Networks_Oversampling_Median Imputation...  
 29   Neural Networks_Oversampling_Median Imputation...  
 30   Neural Networks_Oversampling_Median Imputation...  
 31   Neural Networks_Oversampling_Median Imputation...  
 32   Neural Networks_Oversampling_Median Imputation...  
 33   Neural Networks_Oversampling_Median Imputation...  
 34   Neural Networks_Oversampling_Median Imputation...  
 35   Neural Networks_Oversampling_Median Imputation...  
 36   Neural Networks_Oversampling_Median Imputation...  
 37   Neural Networks_Oversampling_Median Imputation...  
 38   Neural Networks_Oversampling_Median Imputation...  
 39   Neural Networks_Oversampling_Median Imputation...  
 40   Neural Networks_Oversampling_Median Imputation...  
 41   Neural Networks_Oversampling_Median Imputation...  
 42   Neural Networks_Oversampling_Median Imputation...  
 43   Neural Networks_Oversampling_Median Imputation...  
 44   Neural Networks_Oversampling_Median Imputation...  
 45   Neural Networks_Oversampling_Median Imputation...  
 46   Neural Networks_Oversampling_Median Imputation...  
 47   Neural Networks_Oversampling_Median Imputation...  
 48   Neural Networks_Oversampling_Median Imputation...  
 49   Neural Networks_Oversampling_Median Imputation...  
 50   Neural Networks_Oversampling_Median Imputation...  
 51   Neural Networks_Oversampling_Median Imputation...  
 52   Neural Networks_Oversampling_Median Imputation...  
 53   Neural Networks_Oversampling_Median Imputation...  
 54   Neural Networks_Oversampling_Median Imputation...  
 55   Neural Networks_Oversampling_Median Imputation...  
 56   Neural Networks_Oversampling_Median Imputation...  
 57   Neural Networks_Oversampling_Median Imputation...  
 58   Neural Networks_Oversampling_Median Imputation...  
 59   Neural Networks_Oversampling_Median Imputation...  
 60   Neural Networks_Oversampling_Median Imputation...  
 61   Neural Networks_Oversampling_Median Imputation...  
 62   Neural Networks_Oversampling_Median Imputation...  
 63   Neural Networks_Oversampling_Median Imputation...  
 64   Neural Networks_Oversampling_Median Imputation...  
 65   Neural Networks_Oversampling_Median Imputation...  
 66   Neural Networks_Oversampling_Median Imputation...  
 67   Neural Networks_Oversampling_Median Imputation...  
 68   Neural Networks_Oversampling_Median Imputation...  
 69   Neural Networks_Oversampling_Median Imputation...  
 70   Neural Networks_Oversampling_Median Imputation...  
 71   Neural Networks_Oversampling_Median Imputation...  
 72   Neural Networks_Oversampling_Median Imputation...  
 73   Neural Networks_Oversampling_Median Imputation...  
 74   Neural Networks_Oversampling_Median Imputation...  
 75   Neural Networks_Oversampling_Median Imputation...  
 76   Neural Networks_Oversampling_Median Imputation...  
 77   Neural Networks_Oversampling_Median Imputation...  
 78   Neural Networks_Oversampling_Median Imputation...  
 79   Neural Networks_Oversampling_Median Imputation...  
 80   Neural Networks_Oversampling_Median Imputation...  
 81   Neural Networks_Oversampling_Median Imputation...  
 82   Neural Networks_Oversampling_Median Imputation...  
 83   Neural Networks_Oversampling_Median Imputation...  
 84   Neural Networks_Oversampling_Median Imputation...  
 85   Neural Networks_Oversampling_Median Imputation...  
 86   Neural Networks_Oversampling_Median Imputation...  
 87   Neural Networks_Oversampling_Median Imputation...  
 88   Neural Networks_Oversampling_Median Imputation...  
 89   Neural Networks_Oversampling_Median Imputation...  
 90   Neural Networks_Oversampling_Median Imputation...  
 91   Neural Networks_Oversampling_Median Imputation...  
 92   Neural Networks_Oversampling_Median Imputation...  
 93   Neural Networks_Oversampling_Median Imputation...  
 94   Neural Networks_Oversampling_Median Imputation...  
 95   Neural Networks_Oversampling_Median Imputation...  
 96   Neural Networks_Oversampling_Median Imputation...  
 97   Neural Networks_Oversampling_Median Imputation...  
 98   Neural Networks_Oversampling_Median Imputation...  
 99   Neural Networks_Oversampling_Median Imputation...  
 100  Neural Networks_Oversampling_Median Imputation...  
 101  Neural Networks_Oversampling_Median Imputation...  
 102  Neural Networks_Oversampling_Median Imputation...  
 103  Neural Networks_Oversampling_Median Imputation...  
 104  Neural Networks_Oversampling_Median Imputation...  
 105  Neural Networks_Oversampling_Median Imputation...  
 106  Neural Networks_Oversampling_Median Imputation...  
 107  Neural Networks_Oversampling_Median Imputation...  
 108  Neural Networks_Oversampling_Median Imputation...  
 109  Neural Networks_Oversampling_Median Imputation...  
 110  Neural Networks_Oversampling_Median Imputation...  
 111  Neural Networks_Oversampling_Median Imputation...  
 112  Neural Networks_Oversampling_Median Imputation...  
 113  Neural Networks_Oversampling_Median Imputation...  
 114  Neural Networks_Oversampling_Median Imputation...  
 115  Neural Networks_Oversampling_Median Imputation...  
 116  Neural Networks_Oversampling_Median Imputation...  
 117  Neural Networks_Oversampling_Median Imputation...  
 118  Neural Networks_Oversampling_Median Imputation...  
 119  Neural Networks_Oversampling_Median Imputation...  
 120  Neural Networks_Oversampling_Median Imputation...  
 121  Neural Networks_Oversampling_Median Imputation...  
 122  Neural Networks_Oversampling_Median Imputation...  
 123  Neural Networks_Oversampling_Median Imputation...  
 124  Neural Networks_Oversampling_Median Imputation...  
 125  Neural Networks_Oversampling_Median Imputation...  
 126  Neural Networks_Oversampling_Median Imputation...  
 127  Neural Networks_Oversampling_Median Imputation...  
 128  Neural Networks_Oversampling_Median Imputation...  
 129  Neural Networks_Oversampling_Median Imputation...  
 130  Neural Networks_Oversampling_Median Imputation...  
 131  Neural Networks_Oversampling_Median Imputation...  
 132  Neural Networks_Oversampling_Median Imputation...  
 133  Neural Networks_Oversampling_Median Imputation...  
 134  Neural Networks_Oversampling_Median Imputation...  
 135  Neural Networks_Oversampling_Median Imputation...  
 136  Neural Networks_Oversampling_Median Imputation...  
 137  Neural Networks_Oversampling_Median Imputation...  
 138  Neural Networks_Oversampling_Median Imputation...  
 139  Neural Networks_Oversampling_Median Imputation...  
 140  Neural Networks_Oversampling_Median Imputation...  
 141  Neural Networks_Oversampling_Median Imputation...  
 142  Neural Networks_Oversampling_Median Imputation...  
 143  Neural Networks_Oversampling_Median Imputation...  
 144  Neural Networks_Oversampling_Median Imputation...  
 145  Neural Networks_Oversampling_Median Imputation...  
 146  Neural Networks_Oversampling_Median Imputation...  
 147  Neural Networks_Oversampling_Median Imputation...  
 148  Neural Networks_Oversampling_Median Imputation...  
 149  Neural Networks_Oversampling_Median Imputation...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.481169             NaN      Zero Imputation   
 1    Neural Networks  0.082007             NaN      Zero Imputation   
 2    Neural Networks  0.717300             NaN      Zero Imputation   
 3    Neural Networks  0.147186             NaN      Zero Imputation   
 4    Neural Networks  0.583420             NaN      Zero Imputation   
 5    Neural Networks  0.206691             NaN      Zero Imputation   
 6    Neural Networks  0.166841             NaN      Zero Imputation   
 7    Neural Networks       NaN            relu      Zero Imputation   
 8    Neural Networks       NaN          0.0001      Zero Imputation   
 9    Neural Networks       NaN            auto      Zero Imputation   
 10   Neural Networks       NaN             0.9      Zero Imputation   
 11   Neural Networks       NaN           0.999      Zero Imputation   
 12   Neural Networks       NaN           False      Zero Imputation   
 13   Neural Networks       NaN             0.0      Zero Imputation   
 14   Neural Networks       NaN          (100,)      Zero Imputation   
 15   Neural Networks       NaN        constant      Zero Imputation   
 16   Neural Networks       NaN           0.001      Zero Imputation   
 17   Neural Networks       NaN           15000      Zero Imputation   
 18   Neural Networks       NaN             200      Zero Imputation   
 19   Neural Networks       NaN             0.9      Zero Imputation   
 20   Neural Networks       NaN              10      Zero Imputation   
 21   Neural Networks       NaN            True      Zero Imputation   
 22   Neural Networks       NaN             0.5      Zero Imputation   
 23   Neural Networks       NaN            None      Zero Imputation   
 24   Neural Networks       NaN            True      Zero Imputation   
 25   Neural Networks       NaN            adam      Zero Imputation   
 26   Neural Networks       NaN          0.0001      Zero Imputation   
 27   Neural Networks       NaN             0.1      Zero Imputation   
 28   Neural Networks       NaN           False      Zero Imputation   
 29   Neural Networks       NaN           False      Zero Imputation   
 30   Neural Networks  0.755333             NaN      Zero Imputation   
 31   Neural Networks  0.083787             NaN      Zero Imputation   
 32   Neural Networks  0.469512             NaN      Zero Imputation   
 33   Neural Networks  0.142198             NaN      Zero Imputation   
 34   Neural Networks  0.659736             NaN      Zero Imputation   
 35   Neural Networks  0.295882             NaN      Zero Imputation   
 36   Neural Networks  0.319472             NaN      Zero Imputation   
 37   Neural Networks       NaN            relu      Zero Imputation   
 38   Neural Networks       NaN          0.0001      Zero Imputation   
 39   Neural Networks       NaN            auto      Zero Imputation   
 40   Neural Networks       NaN             0.9      Zero Imputation   
 41   Neural Networks       NaN           0.999      Zero Imputation   
 42   Neural Networks       NaN           False      Zero Imputation   
 43   Neural Networks       NaN             0.0      Zero Imputation   
 44   Neural Networks       NaN          (100,)      Zero Imputation   
 45   Neural Networks       NaN        constant      Zero Imputation   
 46   Neural Networks       NaN           0.001      Zero Imputation   
 47   Neural Networks       NaN           15000      Zero Imputation   
 48   Neural Networks       NaN             200      Zero Imputation   
 49   Neural Networks       NaN             0.9      Zero Imputation   
 50   Neural Networks       NaN              10      Zero Imputation   
 51   Neural Networks       NaN            True      Zero Imputation   
 52   Neural Networks       NaN             0.5      Zero Imputation   
 53   Neural Networks       NaN            None      Zero Imputation   
 54   Neural Networks       NaN            True      Zero Imputation   
 55   Neural Networks       NaN            adam      Zero Imputation   
 56   Neural Networks       NaN          0.0001      Zero Imputation   
 57   Neural Networks       NaN             0.1      Zero Imputation   
 58   Neural Networks       NaN           False      Zero Imputation   
 59   Neural Networks       NaN           False      Zero Imputation   
 60   Neural Networks  0.402686             NaN      Zero Imputation   
 61   Neural Networks  0.057799             NaN      Zero Imputation   
 62   Neural Networks  0.727273             NaN      Zero Imputation   
 63   Neural Networks  0.107087             NaN      Zero Imputation   
 64   Neural Networks  0.526794             NaN      Zero Imputation   
 65   Neural Networks  0.137846             NaN      Zero Imputation   
 66   Neural Networks  0.053589             NaN      Zero Imputation   
 67   Neural Networks       NaN            relu      Zero Imputation   
 68   Neural Networks       NaN          0.0001      Zero Imputation   
 69   Neural Networks       NaN            auto      Zero Imputation   
 70   Neural Networks       NaN             0.9      Zero Imputation   
 71   Neural Networks       NaN           0.999      Zero Imputation   
 72   Neural Networks       NaN           False      Zero Imputation   
 73   Neural Networks       NaN             0.0      Zero Imputation   
 74   Neural Networks       NaN          (100,)      Zero Imputation   
 75   Neural Networks       NaN        constant      Zero Imputation   
 76   Neural Networks       NaN           0.001      Zero Imputation   
 77   Neural Networks       NaN           15000      Zero Imputation   
 78   Neural Networks       NaN             200      Zero Imputation   
 79   Neural Networks       NaN             0.9      Zero Imputation   
 80   Neural Networks       NaN              10      Zero Imputation   
 81   Neural Networks       NaN            True      Zero Imputation   
 82   Neural Networks       NaN             0.5      Zero Imputation   
 83   Neural Networks       NaN            None      Zero Imputation   
 84   Neural Networks       NaN            True      Zero Imputation   
 85   Neural Networks       NaN            adam      Zero Imputation   
 86   Neural Networks       NaN          0.0001      Zero Imputation   
 87   Neural Networks       NaN             0.1      Zero Imputation   
 88   Neural Networks       NaN           False      Zero Imputation   
 89   Neural Networks       NaN           False      Zero Imputation   
 90   Neural Networks  0.183614             NaN      Zero Imputation   
 91   Neural Networks  0.055436             NaN      Zero Imputation   
 92   Neural Networks  0.923469             NaN      Zero Imputation   
 93   Neural Networks  0.104594             NaN      Zero Imputation   
 94   Neural Networks  0.474583             NaN      Zero Imputation   
 95   Neural Networks  0.077885             NaN      Zero Imputation   
 96   Neural Networks -0.050833             NaN      Zero Imputation   
 97   Neural Networks       NaN            relu      Zero Imputation   
 98   Neural Networks       NaN          0.0001      Zero Imputation   
 99   Neural Networks       NaN            auto      Zero Imputation   
 100  Neural Networks       NaN             0.9      Zero Imputation   
 101  Neural Networks       NaN           0.999      Zero Imputation   
 102  Neural Networks       NaN           False      Zero Imputation   
 103  Neural Networks       NaN             0.0      Zero Imputation   
 104  Neural Networks       NaN          (100,)      Zero Imputation   
 105  Neural Networks       NaN        constant      Zero Imputation   
 106  Neural Networks       NaN           0.001      Zero Imputation   
 107  Neural Networks       NaN           15000      Zero Imputation   
 108  Neural Networks       NaN             200      Zero Imputation   
 109  Neural Networks       NaN             0.9      Zero Imputation   
 110  Neural Networks       NaN              10      Zero Imputation   
 111  Neural Networks       NaN            True      Zero Imputation   
 112  Neural Networks       NaN             0.5      Zero Imputation   
 113  Neural Networks       NaN            None      Zero Imputation   
 114  Neural Networks       NaN            True      Zero Imputation   
 115  Neural Networks       NaN            adam      Zero Imputation   
 116  Neural Networks       NaN          0.0001      Zero Imputation   
 117  Neural Networks       NaN             0.1      Zero Imputation   
 118  Neural Networks       NaN           False      Zero Imputation   
 119  Neural Networks       NaN           False      Zero Imputation   
 120  Neural Networks  0.556375             NaN      Zero Imputation   
 121  Neural Networks  0.086246             NaN      Zero Imputation   
 122  Neural Networks  0.708333             NaN      Zero Imputation   
 123  Neural Networks  0.153769             NaN      Zero Imputation   
 124  Neural Networks  0.622655             NaN      Zero Imputation   
 125  Neural Networks  0.268979             NaN      Zero Imputation   
 126  Neural Networks  0.245310             NaN      Zero Imputation   
 127  Neural Networks       NaN            relu      Zero Imputation   
 128  Neural Networks       NaN          0.0001      Zero Imputation   
 129  Neural Networks       NaN            auto      Zero Imputation   
 130  Neural Networks       NaN             0.9      Zero Imputation   
 131  Neural Networks       NaN           0.999      Zero Imputation   
 132  Neural Networks       NaN           False      Zero Imputation   
 133  Neural Networks       NaN             0.0      Zero Imputation   
 134  Neural Networks       NaN          (100,)      Zero Imputation   
 135  Neural Networks       NaN        constant      Zero Imputation   
 136  Neural Networks       NaN           0.001      Zero Imputation   
 137  Neural Networks       NaN           15000      Zero Imputation   
 138  Neural Networks       NaN             200      Zero Imputation   
 139  Neural Networks       NaN             0.9      Zero Imputation   
 140  Neural Networks       NaN              10      Zero Imputation   
 141  Neural Networks       NaN            True      Zero Imputation   
 142  Neural Networks       NaN             0.5      Zero Imputation   
 143  Neural Networks       NaN            None      Zero Imputation   
 144  Neural Networks       NaN            True      Zero Imputation   
 145  Neural Networks       NaN            adam      Zero Imputation   
 146  Neural Networks       NaN          0.0001      Zero Imputation   
 147  Neural Networks       NaN             0.1      Zero Imputation   
 148  Neural Networks       NaN           False      Zero Imputation   
 149  Neural Networks       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 125             Undersampling   
 126             Undersampling   
 127             Undersampling   
 128             Undersampling   
 129             Undersampling   
 130             Undersampling   
 131             Undersampling   
 132             Undersampling   
 133             Undersampling   
 134             Undersampling   
 135             Undersampling   
 136             Undersampling   
 137             Undersampling   
 138             Undersampling   
 139             Undersampling   
 140             Undersampling   
 141             Undersampling   
 142             Undersampling   
 143             Undersampling   
 144             Undersampling   
 145             Undersampling   
 146             Undersampling   
 147             Undersampling   
 148             Undersampling   
 149             Undersampling   
 
                                      Model Unique Code  
 0    Neural Networks_Undersampling_Zero Imputation_...  
 1    Neural Networks_Undersampling_Zero Imputation_...  
 2    Neural Networks_Undersampling_Zero Imputation_...  
 3    Neural Networks_Undersampling_Zero Imputation_...  
 4    Neural Networks_Undersampling_Zero Imputation_...  
 5    Neural Networks_Undersampling_Zero Imputation_...  
 6    Neural Networks_Undersampling_Zero Imputation_...  
 7    Neural Networks_Undersampling_Zero Imputation_...  
 8    Neural Networks_Undersampling_Zero Imputation_...  
 9    Neural Networks_Undersampling_Zero Imputation_...  
 10   Neural Networks_Undersampling_Zero Imputation_...  
 11   Neural Networks_Undersampling_Zero Imputation_...  
 12   Neural Networks_Undersampling_Zero Imputation_...  
 13   Neural Networks_Undersampling_Zero Imputation_...  
 14   Neural Networks_Undersampling_Zero Imputation_...  
 15   Neural Networks_Undersampling_Zero Imputation_...  
 16   Neural Networks_Undersampling_Zero Imputation_...  
 17   Neural Networks_Undersampling_Zero Imputation_...  
 18   Neural Networks_Undersampling_Zero Imputation_...  
 19   Neural Networks_Undersampling_Zero Imputation_...  
 20   Neural Networks_Undersampling_Zero Imputation_...  
 21   Neural Networks_Undersampling_Zero Imputation_...  
 22   Neural Networks_Undersampling_Zero Imputation_...  
 23   Neural Networks_Undersampling_Zero Imputation_...  
 24   Neural Networks_Undersampling_Zero Imputation_...  
 25   Neural Networks_Undersampling_Zero Imputation_...  
 26   Neural Networks_Undersampling_Zero Imputation_...  
 27   Neural Networks_Undersampling_Zero Imputation_...  
 28   Neural Networks_Undersampling_Zero Imputation_...  
 29   Neural Networks_Undersampling_Zero Imputation_...  
 30   Neural Networks_Undersampling_Zero Imputation_...  
 31   Neural Networks_Undersampling_Zero Imputation_...  
 32   Neural Networks_Undersampling_Zero Imputation_...  
 33   Neural Networks_Undersampling_Zero Imputation_...  
 34   Neural Networks_Undersampling_Zero Imputation_...  
 35   Neural Networks_Undersampling_Zero Imputation_...  
 36   Neural Networks_Undersampling_Zero Imputation_...  
 37   Neural Networks_Undersampling_Zero Imputation_...  
 38   Neural Networks_Undersampling_Zero Imputation_...  
 39   Neural Networks_Undersampling_Zero Imputation_...  
 40   Neural Networks_Undersampling_Zero Imputation_...  
 41   Neural Networks_Undersampling_Zero Imputation_...  
 42   Neural Networks_Undersampling_Zero Imputation_...  
 43   Neural Networks_Undersampling_Zero Imputation_...  
 44   Neural Networks_Undersampling_Zero Imputation_...  
 45   Neural Networks_Undersampling_Zero Imputation_...  
 46   Neural Networks_Undersampling_Zero Imputation_...  
 47   Neural Networks_Undersampling_Zero Imputation_...  
 48   Neural Networks_Undersampling_Zero Imputation_...  
 49   Neural Networks_Undersampling_Zero Imputation_...  
 50   Neural Networks_Undersampling_Zero Imputation_...  
 51   Neural Networks_Undersampling_Zero Imputation_...  
 52   Neural Networks_Undersampling_Zero Imputation_...  
 53   Neural Networks_Undersampling_Zero Imputation_...  
 54   Neural Networks_Undersampling_Zero Imputation_...  
 55   Neural Networks_Undersampling_Zero Imputation_...  
 56   Neural Networks_Undersampling_Zero Imputation_...  
 57   Neural Networks_Undersampling_Zero Imputation_...  
 58   Neural Networks_Undersampling_Zero Imputation_...  
 59   Neural Networks_Undersampling_Zero Imputation_...  
 60   Neural Networks_Undersampling_Zero Imputation_...  
 61   Neural Networks_Undersampling_Zero Imputation_...  
 62   Neural Networks_Undersampling_Zero Imputation_...  
 63   Neural Networks_Undersampling_Zero Imputation_...  
 64   Neural Networks_Undersampling_Zero Imputation_...  
 65   Neural Networks_Undersampling_Zero Imputation_...  
 66   Neural Networks_Undersampling_Zero Imputation_...  
 67   Neural Networks_Undersampling_Zero Imputation_...  
 68   Neural Networks_Undersampling_Zero Imputation_...  
 69   Neural Networks_Undersampling_Zero Imputation_...  
 70   Neural Networks_Undersampling_Zero Imputation_...  
 71   Neural Networks_Undersampling_Zero Imputation_...  
 72   Neural Networks_Undersampling_Zero Imputation_...  
 73   Neural Networks_Undersampling_Zero Imputation_...  
 74   Neural Networks_Undersampling_Zero Imputation_...  
 75   Neural Networks_Undersampling_Zero Imputation_...  
 76   Neural Networks_Undersampling_Zero Imputation_...  
 77   Neural Networks_Undersampling_Zero Imputation_...  
 78   Neural Networks_Undersampling_Zero Imputation_...  
 79   Neural Networks_Undersampling_Zero Imputation_...  
 80   Neural Networks_Undersampling_Zero Imputation_...  
 81   Neural Networks_Undersampling_Zero Imputation_...  
 82   Neural Networks_Undersampling_Zero Imputation_...  
 83   Neural Networks_Undersampling_Zero Imputation_...  
 84   Neural Networks_Undersampling_Zero Imputation_...  
 85   Neural Networks_Undersampling_Zero Imputation_...  
 86   Neural Networks_Undersampling_Zero Imputation_...  
 87   Neural Networks_Undersampling_Zero Imputation_...  
 88   Neural Networks_Undersampling_Zero Imputation_...  
 89   Neural Networks_Undersampling_Zero Imputation_...  
 90   Neural Networks_Undersampling_Zero Imputation_...  
 91   Neural Networks_Undersampling_Zero Imputation_...  
 92   Neural Networks_Undersampling_Zero Imputation_...  
 93   Neural Networks_Undersampling_Zero Imputation_...  
 94   Neural Networks_Undersampling_Zero Imputation_...  
 95   Neural Networks_Undersampling_Zero Imputation_...  
 96   Neural Networks_Undersampling_Zero Imputation_...  
 97   Neural Networks_Undersampling_Zero Imputation_...  
 98   Neural Networks_Undersampling_Zero Imputation_...  
 99   Neural Networks_Undersampling_Zero Imputation_...  
 100  Neural Networks_Undersampling_Zero Imputation_...  
 101  Neural Networks_Undersampling_Zero Imputation_...  
 102  Neural Networks_Undersampling_Zero Imputation_...  
 103  Neural Networks_Undersampling_Zero Imputation_...  
 104  Neural Networks_Undersampling_Zero Imputation_...  
 105  Neural Networks_Undersampling_Zero Imputation_...  
 106  Neural Networks_Undersampling_Zero Imputation_...  
 107  Neural Networks_Undersampling_Zero Imputation_...  
 108  Neural Networks_Undersampling_Zero Imputation_...  
 109  Neural Networks_Undersampling_Zero Imputation_...  
 110  Neural Networks_Undersampling_Zero Imputation_...  
 111  Neural Networks_Undersampling_Zero Imputation_...  
 112  Neural Networks_Undersampling_Zero Imputation_...  
 113  Neural Networks_Undersampling_Zero Imputation_...  
 114  Neural Networks_Undersampling_Zero Imputation_...  
 115  Neural Networks_Undersampling_Zero Imputation_...  
 116  Neural Networks_Undersampling_Zero Imputation_...  
 117  Neural Networks_Undersampling_Zero Imputation_...  
 118  Neural Networks_Undersampling_Zero Imputation_...  
 119  Neural Networks_Undersampling_Zero Imputation_...  
 120  Neural Networks_Undersampling_Zero Imputation_...  
 121  Neural Networks_Undersampling_Zero Imputation_...  
 122  Neural Networks_Undersampling_Zero Imputation_...  
 123  Neural Networks_Undersampling_Zero Imputation_...  
 124  Neural Networks_Undersampling_Zero Imputation_...  
 125  Neural Networks_Undersampling_Zero Imputation_...  
 126  Neural Networks_Undersampling_Zero Imputation_...  
 127  Neural Networks_Undersampling_Zero Imputation_...  
 128  Neural Networks_Undersampling_Zero Imputation_...  
 129  Neural Networks_Undersampling_Zero Imputation_...  
 130  Neural Networks_Undersampling_Zero Imputation_...  
 131  Neural Networks_Undersampling_Zero Imputation_...  
 132  Neural Networks_Undersampling_Zero Imputation_...  
 133  Neural Networks_Undersampling_Zero Imputation_...  
 134  Neural Networks_Undersampling_Zero Imputation_...  
 135  Neural Networks_Undersampling_Zero Imputation_...  
 136  Neural Networks_Undersampling_Zero Imputation_...  
 137  Neural Networks_Undersampling_Zero Imputation_...  
 138  Neural Networks_Undersampling_Zero Imputation_...  
 139  Neural Networks_Undersampling_Zero Imputation_...  
 140  Neural Networks_Undersampling_Zero Imputation_...  
 141  Neural Networks_Undersampling_Zero Imputation_...  
 142  Neural Networks_Undersampling_Zero Imputation_...  
 143  Neural Networks_Undersampling_Zero Imputation_...  
 144  Neural Networks_Undersampling_Zero Imputation_...  
 145  Neural Networks_Undersampling_Zero Imputation_...  
 146  Neural Networks_Undersampling_Zero Imputation_...  
 147  Neural Networks_Undersampling_Zero Imputation_...  
 148  Neural Networks_Undersampling_Zero Imputation_...  
 149  Neural Networks_Undersampling_Zero Imputation_...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.819068             NaN      Mean Imputation   
 1    Neural Networks  0.092391             NaN      Mean Imputation   
 2    Neural Networks  0.215190             NaN      Mean Imputation   
 3    Neural Networks  0.129278             NaN      Mean Imputation   
 4    Neural Networks  0.635950             NaN      Mean Imputation   
 5    Neural Networks  0.256894             NaN      Mean Imputation   
 6    Neural Networks  0.271901             NaN      Mean Imputation   
 7    Neural Networks       NaN            relu      Mean Imputation   
 8    Neural Networks       NaN          0.0001      Mean Imputation   
 9    Neural Networks       NaN            auto      Mean Imputation   
 10   Neural Networks       NaN             0.9      Mean Imputation   
 11   Neural Networks       NaN           0.999      Mean Imputation   
 12   Neural Networks       NaN           False      Mean Imputation   
 13   Neural Networks       NaN             0.0      Mean Imputation   
 14   Neural Networks       NaN          (100,)      Mean Imputation   
 15   Neural Networks       NaN        constant      Mean Imputation   
 16   Neural Networks       NaN           0.001      Mean Imputation   
 17   Neural Networks       NaN           15000      Mean Imputation   
 18   Neural Networks       NaN             200      Mean Imputation   
 19   Neural Networks       NaN             0.9      Mean Imputation   
 20   Neural Networks       NaN              10      Mean Imputation   
 21   Neural Networks       NaN            True      Mean Imputation   
 22   Neural Networks       NaN             0.5      Mean Imputation   
 23   Neural Networks       NaN            None      Mean Imputation   
 24   Neural Networks       NaN            True      Mean Imputation   
 25   Neural Networks       NaN            adam      Mean Imputation   
 26   Neural Networks       NaN          0.0001      Mean Imputation   
 27   Neural Networks       NaN             0.1      Mean Imputation   
 28   Neural Networks       NaN           False      Mean Imputation   
 29   Neural Networks       NaN           False      Mean Imputation   
 30   Neural Networks  0.391625             NaN      Mean Imputation   
 31   Neural Networks  0.046108             NaN      Mean Imputation   
 32   Neural Networks  0.664634             NaN      Mean Imputation   
 33   Neural Networks  0.086234             NaN      Mean Imputation   
 34   Neural Networks  0.537689             NaN      Mean Imputation   
 35   Neural Networks  0.071306             NaN      Mean Imputation   
 36   Neural Networks  0.075378             NaN      Mean Imputation   
 37   Neural Networks       NaN            relu      Mean Imputation   
 38   Neural Networks       NaN          0.0001      Mean Imputation   
 39   Neural Networks       NaN            auto      Mean Imputation   
 40   Neural Networks       NaN             0.9      Mean Imputation   
 41   Neural Networks       NaN           0.999      Mean Imputation   
 42   Neural Networks       NaN           False      Mean Imputation   
 43   Neural Networks       NaN             0.0      Mean Imputation   
 44   Neural Networks       NaN          (100,)      Mean Imputation   
 45   Neural Networks       NaN        constant      Mean Imputation   
 46   Neural Networks       NaN           0.001      Mean Imputation   
 47   Neural Networks       NaN           15000      Mean Imputation   
 48   Neural Networks       NaN             200      Mean Imputation   
 49   Neural Networks       NaN             0.9      Mean Imputation   
 50   Neural Networks       NaN              10      Mean Imputation   
 51   Neural Networks       NaN            True      Mean Imputation   
 52   Neural Networks       NaN             0.5      Mean Imputation   
 53   Neural Networks       NaN            None      Mean Imputation   
 54   Neural Networks       NaN            True      Mean Imputation   
 55   Neural Networks       NaN            adam      Mean Imputation   
 56   Neural Networks       NaN          0.0001      Mean Imputation   
 57   Neural Networks       NaN             0.1      Mean Imputation   
 58   Neural Networks       NaN           False      Mean Imputation   
 59   Neural Networks       NaN           False      Mean Imputation   
 60   Neural Networks  0.763761             NaN      Mean Imputation   
 61   Neural Networks  0.064951             NaN      Mean Imputation   
 62   Neural Networks  0.283422             NaN      Mean Imputation   
 63   Neural Networks  0.105683             NaN      Mean Imputation   
 64   Neural Networks  0.622312             NaN      Mean Imputation   
 65   Neural Networks  0.267740             NaN      Mean Imputation   
 66   Neural Networks  0.244624             NaN      Mean Imputation   
 67   Neural Networks       NaN            relu      Mean Imputation   
 68   Neural Networks       NaN          0.0001      Mean Imputation   
 69   Neural Networks       NaN            auto      Mean Imputation   
 70   Neural Networks       NaN             0.9      Mean Imputation   
 71   Neural Networks       NaN           0.999      Mean Imputation   
 72   Neural Networks       NaN           False      Mean Imputation   
 73   Neural Networks       NaN             0.0      Mean Imputation   
 74   Neural Networks       NaN          (100,)      Mean Imputation   
 75   Neural Networks       NaN        constant      Mean Imputation   
 76   Neural Networks       NaN           0.001      Mean Imputation   
 77   Neural Networks       NaN           15000      Mean Imputation   
 78   Neural Networks       NaN             200      Mean Imputation   
 79   Neural Networks       NaN             0.9      Mean Imputation   
 80   Neural Networks       NaN              10      Mean Imputation   
 81   Neural Networks       NaN            True      Mean Imputation   
 82   Neural Networks       NaN             0.5      Mean Imputation   
 83   Neural Networks       NaN            None      Mean Imputation   
 84   Neural Networks       NaN            True      Mean Imputation   
 85   Neural Networks       NaN            adam      Mean Imputation   
 86   Neural Networks       NaN          0.0001      Mean Imputation   
 87   Neural Networks       NaN             0.1      Mean Imputation   
 88   Neural Networks       NaN           False      Mean Imputation   
 89   Neural Networks       NaN           False      Mean Imputation   
 90   Neural Networks  0.728135             NaN      Mean Imputation   
 91   Neural Networks  0.060000             NaN      Mean Imputation   
 92   Neural Networks  0.290816             NaN      Mean Imputation   
 93   Neural Networks  0.099476             NaN      Mean Imputation   
 94   Neural Networks  0.589539             NaN      Mean Imputation   
 95   Neural Networks  0.199240             NaN      Mean Imputation   
 96   Neural Networks  0.179079             NaN      Mean Imputation   
 97   Neural Networks       NaN            relu      Mean Imputation   
 98   Neural Networks       NaN          0.0001      Mean Imputation   
 99   Neural Networks       NaN            auto      Mean Imputation   
 100  Neural Networks       NaN             0.9      Mean Imputation   
 101  Neural Networks       NaN           0.999      Mean Imputation   
 102  Neural Networks       NaN           False      Mean Imputation   
 103  Neural Networks       NaN             0.0      Mean Imputation   
 104  Neural Networks       NaN          (100,)      Mean Imputation   
 105  Neural Networks       NaN        constant      Mean Imputation   
 106  Neural Networks       NaN           0.001      Mean Imputation   
 107  Neural Networks       NaN           15000      Mean Imputation   
 108  Neural Networks       NaN             200      Mean Imputation   
 109  Neural Networks       NaN             0.9      Mean Imputation   
 110  Neural Networks       NaN              10      Mean Imputation   
 111  Neural Networks       NaN            True      Mean Imputation   
 112  Neural Networks       NaN             0.5      Mean Imputation   
 113  Neural Networks       NaN            None      Mean Imputation   
 114  Neural Networks       NaN            True      Mean Imputation   
 115  Neural Networks       NaN            adam      Mean Imputation   
 116  Neural Networks       NaN          0.0001      Mean Imputation   
 117  Neural Networks       NaN             0.1      Mean Imputation   
 118  Neural Networks       NaN           False      Mean Imputation   
 119  Neural Networks       NaN           False      Mean Imputation   
 120  Neural Networks  0.265016             NaN      Mean Imputation   
 121  Neural Networks  0.064024             NaN      Mean Imputation   
 122  Neural Networks  0.875000             NaN      Mean Imputation   
 123  Neural Networks  0.119318             NaN      Mean Imputation   
 124  Neural Networks  0.559148             NaN      Mean Imputation   
 125  Neural Networks  0.110227             NaN      Mean Imputation   
 126  Neural Networks  0.118296             NaN      Mean Imputation   
 127  Neural Networks       NaN            relu      Mean Imputation   
 128  Neural Networks       NaN          0.0001      Mean Imputation   
 129  Neural Networks       NaN            auto      Mean Imputation   
 130  Neural Networks       NaN             0.9      Mean Imputation   
 131  Neural Networks       NaN           0.999      Mean Imputation   
 132  Neural Networks       NaN           False      Mean Imputation   
 133  Neural Networks       NaN             0.0      Mean Imputation   
 134  Neural Networks       NaN          (100,)      Mean Imputation   
 135  Neural Networks       NaN        constant      Mean Imputation   
 136  Neural Networks       NaN           0.001      Mean Imputation   
 137  Neural Networks       NaN           15000      Mean Imputation   
 138  Neural Networks       NaN             200      Mean Imputation   
 139  Neural Networks       NaN             0.9      Mean Imputation   
 140  Neural Networks       NaN              10      Mean Imputation   
 141  Neural Networks       NaN            True      Mean Imputation   
 142  Neural Networks       NaN             0.5      Mean Imputation   
 143  Neural Networks       NaN            None      Mean Imputation   
 144  Neural Networks       NaN            True      Mean Imputation   
 145  Neural Networks       NaN            adam      Mean Imputation   
 146  Neural Networks       NaN          0.0001      Mean Imputation   
 147  Neural Networks       NaN             0.1      Mean Imputation   
 148  Neural Networks       NaN           False      Mean Imputation   
 149  Neural Networks       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 125             Undersampling   
 126             Undersampling   
 127             Undersampling   
 128             Undersampling   
 129             Undersampling   
 130             Undersampling   
 131             Undersampling   
 132             Undersampling   
 133             Undersampling   
 134             Undersampling   
 135             Undersampling   
 136             Undersampling   
 137             Undersampling   
 138             Undersampling   
 139             Undersampling   
 140             Undersampling   
 141             Undersampling   
 142             Undersampling   
 143             Undersampling   
 144             Undersampling   
 145             Undersampling   
 146             Undersampling   
 147             Undersampling   
 148             Undersampling   
 149             Undersampling   
 
                                      Model Unique Code  
 0    Neural Networks_Undersampling_Mean Imputation_...  
 1    Neural Networks_Undersampling_Mean Imputation_...  
 2    Neural Networks_Undersampling_Mean Imputation_...  
 3    Neural Networks_Undersampling_Mean Imputation_...  
 4    Neural Networks_Undersampling_Mean Imputation_...  
 5    Neural Networks_Undersampling_Mean Imputation_...  
 6    Neural Networks_Undersampling_Mean Imputation_...  
 7    Neural Networks_Undersampling_Mean Imputation_...  
 8    Neural Networks_Undersampling_Mean Imputation_...  
 9    Neural Networks_Undersampling_Mean Imputation_...  
 10   Neural Networks_Undersampling_Mean Imputation_...  
 11   Neural Networks_Undersampling_Mean Imputation_...  
 12   Neural Networks_Undersampling_Mean Imputation_...  
 13   Neural Networks_Undersampling_Mean Imputation_...  
 14   Neural Networks_Undersampling_Mean Imputation_...  
 15   Neural Networks_Undersampling_Mean Imputation_...  
 16   Neural Networks_Undersampling_Mean Imputation_...  
 17   Neural Networks_Undersampling_Mean Imputation_...  
 18   Neural Networks_Undersampling_Mean Imputation_...  
 19   Neural Networks_Undersampling_Mean Imputation_...  
 20   Neural Networks_Undersampling_Mean Imputation_...  
 21   Neural Networks_Undersampling_Mean Imputation_...  
 22   Neural Networks_Undersampling_Mean Imputation_...  
 23   Neural Networks_Undersampling_Mean Imputation_...  
 24   Neural Networks_Undersampling_Mean Imputation_...  
 25   Neural Networks_Undersampling_Mean Imputation_...  
 26   Neural Networks_Undersampling_Mean Imputation_...  
 27   Neural Networks_Undersampling_Mean Imputation_...  
 28   Neural Networks_Undersampling_Mean Imputation_...  
 29   Neural Networks_Undersampling_Mean Imputation_...  
 30   Neural Networks_Undersampling_Mean Imputation_...  
 31   Neural Networks_Undersampling_Mean Imputation_...  
 32   Neural Networks_Undersampling_Mean Imputation_...  
 33   Neural Networks_Undersampling_Mean Imputation_...  
 34   Neural Networks_Undersampling_Mean Imputation_...  
 35   Neural Networks_Undersampling_Mean Imputation_...  
 36   Neural Networks_Undersampling_Mean Imputation_...  
 37   Neural Networks_Undersampling_Mean Imputation_...  
 38   Neural Networks_Undersampling_Mean Imputation_...  
 39   Neural Networks_Undersampling_Mean Imputation_...  
 40   Neural Networks_Undersampling_Mean Imputation_...  
 41   Neural Networks_Undersampling_Mean Imputation_...  
 42   Neural Networks_Undersampling_Mean Imputation_...  
 43   Neural Networks_Undersampling_Mean Imputation_...  
 44   Neural Networks_Undersampling_Mean Imputation_...  
 45   Neural Networks_Undersampling_Mean Imputation_...  
 46   Neural Networks_Undersampling_Mean Imputation_...  
 47   Neural Networks_Undersampling_Mean Imputation_...  
 48   Neural Networks_Undersampling_Mean Imputation_...  
 49   Neural Networks_Undersampling_Mean Imputation_...  
 50   Neural Networks_Undersampling_Mean Imputation_...  
 51   Neural Networks_Undersampling_Mean Imputation_...  
 52   Neural Networks_Undersampling_Mean Imputation_...  
 53   Neural Networks_Undersampling_Mean Imputation_...  
 54   Neural Networks_Undersampling_Mean Imputation_...  
 55   Neural Networks_Undersampling_Mean Imputation_...  
 56   Neural Networks_Undersampling_Mean Imputation_...  
 57   Neural Networks_Undersampling_Mean Imputation_...  
 58   Neural Networks_Undersampling_Mean Imputation_...  
 59   Neural Networks_Undersampling_Mean Imputation_...  
 60   Neural Networks_Undersampling_Mean Imputation_...  
 61   Neural Networks_Undersampling_Mean Imputation_...  
 62   Neural Networks_Undersampling_Mean Imputation_...  
 63   Neural Networks_Undersampling_Mean Imputation_...  
 64   Neural Networks_Undersampling_Mean Imputation_...  
 65   Neural Networks_Undersampling_Mean Imputation_...  
 66   Neural Networks_Undersampling_Mean Imputation_...  
 67   Neural Networks_Undersampling_Mean Imputation_...  
 68   Neural Networks_Undersampling_Mean Imputation_...  
 69   Neural Networks_Undersampling_Mean Imputation_...  
 70   Neural Networks_Undersampling_Mean Imputation_...  
 71   Neural Networks_Undersampling_Mean Imputation_...  
 72   Neural Networks_Undersampling_Mean Imputation_...  
 73   Neural Networks_Undersampling_Mean Imputation_...  
 74   Neural Networks_Undersampling_Mean Imputation_...  
 75   Neural Networks_Undersampling_Mean Imputation_...  
 76   Neural Networks_Undersampling_Mean Imputation_...  
 77   Neural Networks_Undersampling_Mean Imputation_...  
 78   Neural Networks_Undersampling_Mean Imputation_...  
 79   Neural Networks_Undersampling_Mean Imputation_...  
 80   Neural Networks_Undersampling_Mean Imputation_...  
 81   Neural Networks_Undersampling_Mean Imputation_...  
 82   Neural Networks_Undersampling_Mean Imputation_...  
 83   Neural Networks_Undersampling_Mean Imputation_...  
 84   Neural Networks_Undersampling_Mean Imputation_...  
 85   Neural Networks_Undersampling_Mean Imputation_...  
 86   Neural Networks_Undersampling_Mean Imputation_...  
 87   Neural Networks_Undersampling_Mean Imputation_...  
 88   Neural Networks_Undersampling_Mean Imputation_...  
 89   Neural Networks_Undersampling_Mean Imputation_...  
 90   Neural Networks_Undersampling_Mean Imputation_...  
 91   Neural Networks_Undersampling_Mean Imputation_...  
 92   Neural Networks_Undersampling_Mean Imputation_...  
 93   Neural Networks_Undersampling_Mean Imputation_...  
 94   Neural Networks_Undersampling_Mean Imputation_...  
 95   Neural Networks_Undersampling_Mean Imputation_...  
 96   Neural Networks_Undersampling_Mean Imputation_...  
 97   Neural Networks_Undersampling_Mean Imputation_...  
 98   Neural Networks_Undersampling_Mean Imputation_...  
 99   Neural Networks_Undersampling_Mean Imputation_...  
 100  Neural Networks_Undersampling_Mean Imputation_...  
 101  Neural Networks_Undersampling_Mean Imputation_...  
 102  Neural Networks_Undersampling_Mean Imputation_...  
 103  Neural Networks_Undersampling_Mean Imputation_...  
 104  Neural Networks_Undersampling_Mean Imputation_...  
 105  Neural Networks_Undersampling_Mean Imputation_...  
 106  Neural Networks_Undersampling_Mean Imputation_...  
 107  Neural Networks_Undersampling_Mean Imputation_...  
 108  Neural Networks_Undersampling_Mean Imputation_...  
 109  Neural Networks_Undersampling_Mean Imputation_...  
 110  Neural Networks_Undersampling_Mean Imputation_...  
 111  Neural Networks_Undersampling_Mean Imputation_...  
 112  Neural Networks_Undersampling_Mean Imputation_...  
 113  Neural Networks_Undersampling_Mean Imputation_...  
 114  Neural Networks_Undersampling_Mean Imputation_...  
 115  Neural Networks_Undersampling_Mean Imputation_...  
 116  Neural Networks_Undersampling_Mean Imputation_...  
 117  Neural Networks_Undersampling_Mean Imputation_...  
 118  Neural Networks_Undersampling_Mean Imputation_...  
 119  Neural Networks_Undersampling_Mean Imputation_...  
 120  Neural Networks_Undersampling_Mean Imputation_...  
 121  Neural Networks_Undersampling_Mean Imputation_...  
 122  Neural Networks_Undersampling_Mean Imputation_...  
 123  Neural Networks_Undersampling_Mean Imputation_...  
 124  Neural Networks_Undersampling_Mean Imputation_...  
 125  Neural Networks_Undersampling_Mean Imputation_...  
 126  Neural Networks_Undersampling_Mean Imputation_...  
 127  Neural Networks_Undersampling_Mean Imputation_...  
 128  Neural Networks_Undersampling_Mean Imputation_...  
 129  Neural Networks_Undersampling_Mean Imputation_...  
 130  Neural Networks_Undersampling_Mean Imputation_...  
 131  Neural Networks_Undersampling_Mean Imputation_...  
 132  Neural Networks_Undersampling_Mean Imputation_...  
 133  Neural Networks_Undersampling_Mean Imputation_...  
 134  Neural Networks_Undersampling_Mean Imputation_...  
 135  Neural Networks_Undersampling_Mean Imputation_...  
 136  Neural Networks_Undersampling_Mean Imputation_...  
 137  Neural Networks_Undersampling_Mean Imputation_...  
 138  Neural Networks_Undersampling_Mean Imputation_...  
 139  Neural Networks_Undersampling_Mean Imputation_...  
 140  Neural Networks_Undersampling_Mean Imputation_...  
 141  Neural Networks_Undersampling_Mean Imputation_...  
 142  Neural Networks_Undersampling_Mean Imputation_...  
 143  Neural Networks_Undersampling_Mean Imputation_...  
 144  Neural Networks_Undersampling_Mean Imputation_...  
 145  Neural Networks_Undersampling_Mean Imputation_...  
 146  Neural Networks_Undersampling_Mean Imputation_...  
 147  Neural Networks_Undersampling_Mean Imputation_...  
 148  Neural Networks_Undersampling_Mean Imputation_...  
 149  Neural Networks_Undersampling_Mean Imputation_...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.347906             NaN    Median Imputation   
 1    Neural Networks  0.071237             NaN    Median Imputation   
 2    Neural Networks  0.784810             NaN    Median Imputation   
 3    Neural Networks  0.130618             NaN    Median Imputation   
 4    Neural Networks  0.557143             NaN    Median Imputation   
 5    Neural Networks  0.111578             NaN    Median Imputation   
 6    Neural Networks  0.114286             NaN    Median Imputation   
 7    Neural Networks       NaN            relu    Median Imputation   
 8    Neural Networks       NaN          0.0001    Median Imputation   
 9    Neural Networks       NaN            auto    Median Imputation   
 10   Neural Networks       NaN             0.9    Median Imputation   
 11   Neural Networks       NaN           0.999    Median Imputation   
 12   Neural Networks       NaN           False    Median Imputation   
 13   Neural Networks       NaN             0.0    Median Imputation   
 14   Neural Networks       NaN          (100,)    Median Imputation   
 15   Neural Networks       NaN        constant    Median Imputation   
 16   Neural Networks       NaN           0.001    Median Imputation   
 17   Neural Networks       NaN           15000    Median Imputation   
 18   Neural Networks       NaN             200    Median Imputation   
 19   Neural Networks       NaN             0.9    Median Imputation   
 20   Neural Networks       NaN              10    Median Imputation   
 21   Neural Networks       NaN            True    Median Imputation   
 22   Neural Networks       NaN             0.5    Median Imputation   
 23   Neural Networks       NaN            None    Median Imputation   
 24   Neural Networks       NaN            True    Median Imputation   
 25   Neural Networks       NaN            adam    Median Imputation   
 26   Neural Networks       NaN          0.0001    Median Imputation   
 27   Neural Networks       NaN             0.1    Median Imputation   
 28   Neural Networks       NaN           False    Median Imputation   
 29   Neural Networks       NaN           False    Median Imputation   
 30   Neural Networks  0.489860             NaN    Median Imputation   
 31   Neural Networks  0.058296             NaN    Median Imputation   
 32   Neural Networks  0.713415             NaN    Median Imputation   
 33   Neural Networks  0.107784             NaN    Median Imputation   
 34   Neural Networks  0.614279             NaN    Median Imputation   
 35   Neural Networks  0.200023             NaN    Median Imputation   
 36   Neural Networks  0.228559             NaN    Median Imputation   
 37   Neural Networks       NaN            relu    Median Imputation   
 38   Neural Networks       NaN          0.0001    Median Imputation   
 39   Neural Networks       NaN            auto    Median Imputation   
 40   Neural Networks       NaN             0.9    Median Imputation   
 41   Neural Networks       NaN           0.999    Median Imputation   
 42   Neural Networks       NaN           False    Median Imputation   
 43   Neural Networks       NaN             0.0    Median Imputation   
 44   Neural Networks       NaN          (100,)    Median Imputation   
 45   Neural Networks       NaN        constant    Median Imputation   
 46   Neural Networks       NaN           0.001    Median Imputation   
 47   Neural Networks       NaN           15000    Median Imputation   
 48   Neural Networks       NaN             200    Median Imputation   
 49   Neural Networks       NaN             0.9    Median Imputation   
 50   Neural Networks       NaN              10    Median Imputation   
 51   Neural Networks       NaN            True    Median Imputation   
 52   Neural Networks       NaN             0.5    Median Imputation   
 53   Neural Networks       NaN            None    Median Imputation   
 54   Neural Networks       NaN            True    Median Imputation   
 55   Neural Networks       NaN            adam    Median Imputation   
 56   Neural Networks       NaN          0.0001    Median Imputation   
 57   Neural Networks       NaN             0.1    Median Imputation   
 58   Neural Networks       NaN           False    Median Imputation   
 59   Neural Networks       NaN           False    Median Imputation   
 60   Neural Networks  0.575191             NaN    Median Imputation   
 61   Neural Networks  0.071514             NaN    Median Imputation   
 62   Neural Networks  0.636364             NaN    Median Imputation   
 63   Neural Networks  0.128579             NaN    Median Imputation   
 64   Neural Networks  0.648829             NaN    Median Imputation   
 65   Neural Networks  0.240617             NaN    Median Imputation   
 66   Neural Networks  0.297658             NaN    Median Imputation   
 67   Neural Networks       NaN            relu    Median Imputation   
 68   Neural Networks       NaN          0.0001    Median Imputation   
 69   Neural Networks       NaN            auto    Median Imputation   
 70   Neural Networks       NaN             0.9    Median Imputation   
 71   Neural Networks       NaN           0.999    Median Imputation   
 72   Neural Networks       NaN           False    Median Imputation   
 73   Neural Networks       NaN             0.0    Median Imputation   
 74   Neural Networks       NaN          (100,)    Median Imputation   
 75   Neural Networks       NaN        constant    Median Imputation   
 76   Neural Networks       NaN           0.001    Median Imputation   
 77   Neural Networks       NaN           15000    Median Imputation   
 78   Neural Networks       NaN             200    Median Imputation   
 79   Neural Networks       NaN             0.9    Median Imputation   
 80   Neural Networks       NaN              10    Median Imputation   
 81   Neural Networks       NaN            True    Median Imputation   
 82   Neural Networks       NaN             0.5    Median Imputation   
 83   Neural Networks       NaN            None    Median Imputation   
 84   Neural Networks       NaN            True    Median Imputation   
 85   Neural Networks       NaN            adam    Median Imputation   
 86   Neural Networks       NaN          0.0001    Median Imputation   
 87   Neural Networks       NaN             0.1    Median Imputation   
 88   Neural Networks       NaN           False    Median Imputation   
 89   Neural Networks       NaN           False    Median Imputation   
 90   Neural Networks  0.699157             NaN    Median Imputation   
 91   Neural Networks  0.059590             NaN    Median Imputation   
 92   Neural Networks  0.326531             NaN    Median Imputation   
 93   Neural Networks  0.100787             NaN    Median Imputation   
 94   Neural Networks  0.593848             NaN    Median Imputation   
 95   Neural Networks  0.200181             NaN    Median Imputation   
 96   Neural Networks  0.187697             NaN    Median Imputation   
 97   Neural Networks       NaN            relu    Median Imputation   
 98   Neural Networks       NaN          0.0001    Median Imputation   
 99   Neural Networks       NaN            auto    Median Imputation   
 100  Neural Networks       NaN             0.9    Median Imputation   
 101  Neural Networks       NaN           0.999    Median Imputation   
 102  Neural Networks       NaN           False    Median Imputation   
 103  Neural Networks       NaN             0.0    Median Imputation   
 104  Neural Networks       NaN          (100,)    Median Imputation   
 105  Neural Networks       NaN        constant    Median Imputation   
 106  Neural Networks       NaN           0.001    Median Imputation   
 107  Neural Networks       NaN           15000    Median Imputation   
 108  Neural Networks       NaN             200    Median Imputation   
 109  Neural Networks       NaN             0.9    Median Imputation   
 110  Neural Networks       NaN              10    Median Imputation   
 111  Neural Networks       NaN            True    Median Imputation   
 112  Neural Networks       NaN             0.5    Median Imputation   
 113  Neural Networks       NaN            None    Median Imputation   
 114  Neural Networks       NaN            True    Median Imputation   
 115  Neural Networks       NaN            adam    Median Imputation   
 116  Neural Networks       NaN          0.0001    Median Imputation   
 117  Neural Networks       NaN             0.1    Median Imputation   
 118  Neural Networks       NaN           False    Median Imputation   
 119  Neural Networks       NaN           False    Median Imputation   
 120  Neural Networks  0.572445             NaN    Median Imputation   
 121  Neural Networks  0.073895             NaN    Median Imputation   
 122  Neural Networks  0.564815             NaN    Median Imputation   
 123  Neural Networks  0.130691             NaN    Median Imputation   
 124  Neural Networks  0.606509             NaN    Median Imputation   
 125  Neural Networks  0.227343             NaN    Median Imputation   
 126  Neural Networks  0.213019             NaN    Median Imputation   
 127  Neural Networks       NaN            relu    Median Imputation   
 128  Neural Networks       NaN          0.0001    Median Imputation   
 129  Neural Networks       NaN            auto    Median Imputation   
 130  Neural Networks       NaN             0.9    Median Imputation   
 131  Neural Networks       NaN           0.999    Median Imputation   
 132  Neural Networks       NaN           False    Median Imputation   
 133  Neural Networks       NaN             0.0    Median Imputation   
 134  Neural Networks       NaN          (100,)    Median Imputation   
 135  Neural Networks       NaN        constant    Median Imputation   
 136  Neural Networks       NaN           0.001    Median Imputation   
 137  Neural Networks       NaN           15000    Median Imputation   
 138  Neural Networks       NaN             200    Median Imputation   
 139  Neural Networks       NaN             0.9    Median Imputation   
 140  Neural Networks       NaN              10    Median Imputation   
 141  Neural Networks       NaN            True    Median Imputation   
 142  Neural Networks       NaN             0.5    Median Imputation   
 143  Neural Networks       NaN            None    Median Imputation   
 144  Neural Networks       NaN            True    Median Imputation   
 145  Neural Networks       NaN            adam    Median Imputation   
 146  Neural Networks       NaN          0.0001    Median Imputation   
 147  Neural Networks       NaN             0.1    Median Imputation   
 148  Neural Networks       NaN           False    Median Imputation   
 149  Neural Networks       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 125             Undersampling   
 126             Undersampling   
 127             Undersampling   
 128             Undersampling   
 129             Undersampling   
 130             Undersampling   
 131             Undersampling   
 132             Undersampling   
 133             Undersampling   
 134             Undersampling   
 135             Undersampling   
 136             Undersampling   
 137             Undersampling   
 138             Undersampling   
 139             Undersampling   
 140             Undersampling   
 141             Undersampling   
 142             Undersampling   
 143             Undersampling   
 144             Undersampling   
 145             Undersampling   
 146             Undersampling   
 147             Undersampling   
 148             Undersampling   
 149             Undersampling   
 
                                      Model Unique Code  
 0    Neural Networks_Undersampling_Median Imputatio...  
 1    Neural Networks_Undersampling_Median Imputatio...  
 2    Neural Networks_Undersampling_Median Imputatio...  
 3    Neural Networks_Undersampling_Median Imputatio...  
 4    Neural Networks_Undersampling_Median Imputatio...  
 5    Neural Networks_Undersampling_Median Imputatio...  
 6    Neural Networks_Undersampling_Median Imputatio...  
 7    Neural Networks_Undersampling_Median Imputatio...  
 8    Neural Networks_Undersampling_Median Imputatio...  
 9    Neural Networks_Undersampling_Median Imputatio...  
 10   Neural Networks_Undersampling_Median Imputatio...  
 11   Neural Networks_Undersampling_Median Imputatio...  
 12   Neural Networks_Undersampling_Median Imputatio...  
 13   Neural Networks_Undersampling_Median Imputatio...  
 14   Neural Networks_Undersampling_Median Imputatio...  
 15   Neural Networks_Undersampling_Median Imputatio...  
 16   Neural Networks_Undersampling_Median Imputatio...  
 17   Neural Networks_Undersampling_Median Imputatio...  
 18   Neural Networks_Undersampling_Median Imputatio...  
 19   Neural Networks_Undersampling_Median Imputatio...  
 20   Neural Networks_Undersampling_Median Imputatio...  
 21   Neural Networks_Undersampling_Median Imputatio...  
 22   Neural Networks_Undersampling_Median Imputatio...  
 23   Neural Networks_Undersampling_Median Imputatio...  
 24   Neural Networks_Undersampling_Median Imputatio...  
 25   Neural Networks_Undersampling_Median Imputatio...  
 26   Neural Networks_Undersampling_Median Imputatio...  
 27   Neural Networks_Undersampling_Median Imputatio...  
 28   Neural Networks_Undersampling_Median Imputatio...  
 29   Neural Networks_Undersampling_Median Imputatio...  
 30   Neural Networks_Undersampling_Median Imputatio...  
 31   Neural Networks_Undersampling_Median Imputatio...  
 32   Neural Networks_Undersampling_Median Imputatio...  
 33   Neural Networks_Undersampling_Median Imputatio...  
 34   Neural Networks_Undersampling_Median Imputatio...  
 35   Neural Networks_Undersampling_Median Imputatio...  
 36   Neural Networks_Undersampling_Median Imputatio...  
 37   Neural Networks_Undersampling_Median Imputatio...  
 38   Neural Networks_Undersampling_Median Imputatio...  
 39   Neural Networks_Undersampling_Median Imputatio...  
 40   Neural Networks_Undersampling_Median Imputatio...  
 41   Neural Networks_Undersampling_Median Imputatio...  
 42   Neural Networks_Undersampling_Median Imputatio...  
 43   Neural Networks_Undersampling_Median Imputatio...  
 44   Neural Networks_Undersampling_Median Imputatio...  
 45   Neural Networks_Undersampling_Median Imputatio...  
 46   Neural Networks_Undersampling_Median Imputatio...  
 47   Neural Networks_Undersampling_Median Imputatio...  
 48   Neural Networks_Undersampling_Median Imputatio...  
 49   Neural Networks_Undersampling_Median Imputatio...  
 50   Neural Networks_Undersampling_Median Imputatio...  
 51   Neural Networks_Undersampling_Median Imputatio...  
 52   Neural Networks_Undersampling_Median Imputatio...  
 53   Neural Networks_Undersampling_Median Imputatio...  
 54   Neural Networks_Undersampling_Median Imputatio...  
 55   Neural Networks_Undersampling_Median Imputatio...  
 56   Neural Networks_Undersampling_Median Imputatio...  
 57   Neural Networks_Undersampling_Median Imputatio...  
 58   Neural Networks_Undersampling_Median Imputatio...  
 59   Neural Networks_Undersampling_Median Imputatio...  
 60   Neural Networks_Undersampling_Median Imputatio...  
 61   Neural Networks_Undersampling_Median Imputatio...  
 62   Neural Networks_Undersampling_Median Imputatio...  
 63   Neural Networks_Undersampling_Median Imputatio...  
 64   Neural Networks_Undersampling_Median Imputatio...  
 65   Neural Networks_Undersampling_Median Imputatio...  
 66   Neural Networks_Undersampling_Median Imputatio...  
 67   Neural Networks_Undersampling_Median Imputatio...  
 68   Neural Networks_Undersampling_Median Imputatio...  
 69   Neural Networks_Undersampling_Median Imputatio...  
 70   Neural Networks_Undersampling_Median Imputatio...  
 71   Neural Networks_Undersampling_Median Imputatio...  
 72   Neural Networks_Undersampling_Median Imputatio...  
 73   Neural Networks_Undersampling_Median Imputatio...  
 74   Neural Networks_Undersampling_Median Imputatio...  
 75   Neural Networks_Undersampling_Median Imputatio...  
 76   Neural Networks_Undersampling_Median Imputatio...  
 77   Neural Networks_Undersampling_Median Imputatio...  
 78   Neural Networks_Undersampling_Median Imputatio...  
 79   Neural Networks_Undersampling_Median Imputatio...  
 80   Neural Networks_Undersampling_Median Imputatio...  
 81   Neural Networks_Undersampling_Median Imputatio...  
 82   Neural Networks_Undersampling_Median Imputatio...  
 83   Neural Networks_Undersampling_Median Imputatio...  
 84   Neural Networks_Undersampling_Median Imputatio...  
 85   Neural Networks_Undersampling_Median Imputatio...  
 86   Neural Networks_Undersampling_Median Imputatio...  
 87   Neural Networks_Undersampling_Median Imputatio...  
 88   Neural Networks_Undersampling_Median Imputatio...  
 89   Neural Networks_Undersampling_Median Imputatio...  
 90   Neural Networks_Undersampling_Median Imputatio...  
 91   Neural Networks_Undersampling_Median Imputatio...  
 92   Neural Networks_Undersampling_Median Imputatio...  
 93   Neural Networks_Undersampling_Median Imputatio...  
 94   Neural Networks_Undersampling_Median Imputatio...  
 95   Neural Networks_Undersampling_Median Imputatio...  
 96   Neural Networks_Undersampling_Median Imputatio...  
 97   Neural Networks_Undersampling_Median Imputatio...  
 98   Neural Networks_Undersampling_Median Imputatio...  
 99   Neural Networks_Undersampling_Median Imputatio...  
 100  Neural Networks_Undersampling_Median Imputatio...  
 101  Neural Networks_Undersampling_Median Imputatio...  
 102  Neural Networks_Undersampling_Median Imputatio...  
 103  Neural Networks_Undersampling_Median Imputatio...  
 104  Neural Networks_Undersampling_Median Imputatio...  
 105  Neural Networks_Undersampling_Median Imputatio...  
 106  Neural Networks_Undersampling_Median Imputatio...  
 107  Neural Networks_Undersampling_Median Imputatio...  
 108  Neural Networks_Undersampling_Median Imputatio...  
 109  Neural Networks_Undersampling_Median Imputatio...  
 110  Neural Networks_Undersampling_Median Imputatio...  
 111  Neural Networks_Undersampling_Median Imputatio...  
 112  Neural Networks_Undersampling_Median Imputatio...  
 113  Neural Networks_Undersampling_Median Imputatio...  
 114  Neural Networks_Undersampling_Median Imputatio...  
 115  Neural Networks_Undersampling_Median Imputatio...  
 116  Neural Networks_Undersampling_Median Imputatio...  
 117  Neural Networks_Undersampling_Median Imputatio...  
 118  Neural Networks_Undersampling_Median Imputatio...  
 119  Neural Networks_Undersampling_Median Imputatio...  
 120  Neural Networks_Undersampling_Median Imputatio...  
 121  Neural Networks_Undersampling_Median Imputatio...  
 122  Neural Networks_Undersampling_Median Imputatio...  
 123  Neural Networks_Undersampling_Median Imputatio...  
 124  Neural Networks_Undersampling_Median Imputatio...  
 125  Neural Networks_Undersampling_Median Imputatio...  
 126  Neural Networks_Undersampling_Median Imputatio...  
 127  Neural Networks_Undersampling_Median Imputatio...  
 128  Neural Networks_Undersampling_Median Imputatio...  
 129  Neural Networks_Undersampling_Median Imputatio...  
 130  Neural Networks_Undersampling_Median Imputatio...  
 131  Neural Networks_Undersampling_Median Imputatio...  
 132  Neural Networks_Undersampling_Median Imputatio...  
 133  Neural Networks_Undersampling_Median Imputatio...  
 134  Neural Networks_Undersampling_Median Imputatio...  
 135  Neural Networks_Undersampling_Median Imputatio...  
 136  Neural Networks_Undersampling_Median Imputatio...  
 137  Neural Networks_Undersampling_Median Imputatio...  
 138  Neural Networks_Undersampling_Median Imputatio...  
 139  Neural Networks_Undersampling_Median Imputatio...  
 140  Neural Networks_Undersampling_Median Imputatio...  
 141  Neural Networks_Undersampling_Median Imputatio...  
 142  Neural Networks_Undersampling_Median Imputatio...  
 143  Neural Networks_Undersampling_Median Imputatio...  
 144  Neural Networks_Undersampling_Median Imputatio...  
 145  Neural Networks_Undersampling_Median Imputatio...  
 146  Neural Networks_Undersampling_Median Imputatio...  
 147  Neural Networks_Undersampling_Median Imputatio...  
 148  Neural Networks_Undersampling_Median Imputatio...  
 149  Neural Networks_Undersampling_Median Imputatio...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.741902             NaN      Zero Imputation   
 1    Neural Networks  0.107709             NaN      Zero Imputation   
 2    Neural Networks  0.430380             NaN      Zero Imputation   
 3    Neural Networks  0.172297             NaN      Zero Imputation   
 4    Neural Networks  0.628136             NaN      Zero Imputation   
 5    Neural Networks  0.226666             NaN      Zero Imputation   
 6    Neural Networks  0.256271             NaN      Zero Imputation   
 7    Neural Networks       NaN            relu      Zero Imputation   
 8    Neural Networks       NaN          0.0001      Zero Imputation   
 9    Neural Networks       NaN            auto      Zero Imputation   
 10   Neural Networks       NaN             0.9      Zero Imputation   
 11   Neural Networks       NaN           0.999      Zero Imputation   
 12   Neural Networks       NaN           False      Zero Imputation   
 13   Neural Networks       NaN             0.0      Zero Imputation   
 14   Neural Networks       NaN          (100,)      Zero Imputation   
 15   Neural Networks       NaN        constant      Zero Imputation   
 16   Neural Networks       NaN           0.001      Zero Imputation   
 17   Neural Networks       NaN           15000      Zero Imputation   
 18   Neural Networks       NaN             200      Zero Imputation   
 19   Neural Networks       NaN             0.9      Zero Imputation   
 20   Neural Networks       NaN              10      Zero Imputation   
 21   Neural Networks       NaN            True      Zero Imputation   
 22   Neural Networks       NaN             0.5      Zero Imputation   
 23   Neural Networks       NaN            None      Zero Imputation   
 24   Neural Networks       NaN            True      Zero Imputation   
 25   Neural Networks       NaN            adam      Zero Imputation   
 26   Neural Networks       NaN          0.0001      Zero Imputation   
 27   Neural Networks       NaN             0.1      Zero Imputation   
 28   Neural Networks       NaN           False      Zero Imputation   
 29   Neural Networks       NaN           False      Zero Imputation   
 30   Neural Networks  0.750066             NaN      Zero Imputation   
 31   Neural Networks  0.081110             NaN      Zero Imputation   
 32   Neural Networks  0.463415             NaN      Zero Imputation   
 33   Neural Networks  0.138056             NaN      Zero Imputation   
 34   Neural Networks  0.663222             NaN      Zero Imputation   
 35   Neural Networks  0.299903             NaN      Zero Imputation   
 36   Neural Networks  0.326444             NaN      Zero Imputation   
 37   Neural Networks       NaN            relu      Zero Imputation   
 38   Neural Networks       NaN          0.0001      Zero Imputation   
 39   Neural Networks       NaN            auto      Zero Imputation   
 40   Neural Networks       NaN             0.9      Zero Imputation   
 41   Neural Networks       NaN           0.999      Zero Imputation   
 42   Neural Networks       NaN           False      Zero Imputation   
 43   Neural Networks       NaN             0.0      Zero Imputation   
 44   Neural Networks       NaN          (100,)      Zero Imputation   
 45   Neural Networks       NaN        constant      Zero Imputation   
 46   Neural Networks       NaN           0.001      Zero Imputation   
 47   Neural Networks       NaN           15000      Zero Imputation   
 48   Neural Networks       NaN             200      Zero Imputation   
 49   Neural Networks       NaN             0.9      Zero Imputation   
 50   Neural Networks       NaN              10      Zero Imputation   
 51   Neural Networks       NaN            True      Zero Imputation   
 52   Neural Networks       NaN             0.5      Zero Imputation   
 53   Neural Networks       NaN            None      Zero Imputation   
 54   Neural Networks       NaN            True      Zero Imputation   
 55   Neural Networks       NaN            adam      Zero Imputation   
 56   Neural Networks       NaN          0.0001      Zero Imputation   
 57   Neural Networks       NaN             0.1      Zero Imputation   
 58   Neural Networks       NaN           False      Zero Imputation   
 59   Neural Networks       NaN           False      Zero Imputation   
 60   Neural Networks  0.834343             NaN      Zero Imputation   
 61   Neural Networks  0.096715             NaN      Zero Imputation   
 62   Neural Networks  0.283422             NaN      Zero Imputation   
 63   Neural Networks  0.144218             NaN      Zero Imputation   
 64   Neural Networks  0.649138             NaN      Zero Imputation   
 65   Neural Networks  0.269494             NaN      Zero Imputation   
 66   Neural Networks  0.298276             NaN      Zero Imputation   
 67   Neural Networks       NaN            relu      Zero Imputation   
 68   Neural Networks       NaN          0.0001      Zero Imputation   
 69   Neural Networks       NaN            auto      Zero Imputation   
 70   Neural Networks       NaN             0.9      Zero Imputation   
 71   Neural Networks       NaN           0.999      Zero Imputation   
 72   Neural Networks       NaN           False      Zero Imputation   
 73   Neural Networks       NaN             0.0      Zero Imputation   
 74   Neural Networks       NaN          (100,)      Zero Imputation   
 75   Neural Networks       NaN        constant      Zero Imputation   
 76   Neural Networks       NaN           0.001      Zero Imputation   
 77   Neural Networks       NaN           15000      Zero Imputation   
 78   Neural Networks       NaN             200      Zero Imputation   
 79   Neural Networks       NaN             0.9      Zero Imputation   
 80   Neural Networks       NaN              10      Zero Imputation   
 81   Neural Networks       NaN            True      Zero Imputation   
 82   Neural Networks       NaN             0.5      Zero Imputation   
 83   Neural Networks       NaN            None      Zero Imputation   
 84   Neural Networks       NaN            True      Zero Imputation   
 85   Neural Networks       NaN            adam      Zero Imputation   
 86   Neural Networks       NaN          0.0001      Zero Imputation   
 87   Neural Networks       NaN             0.1      Zero Imputation   
 88   Neural Networks       NaN           False      Zero Imputation   
 89   Neural Networks       NaN           False      Zero Imputation   
 90   Neural Networks  0.723920             NaN      Zero Imputation   
 91   Neural Networks  0.064417             NaN      Zero Imputation   
 92   Neural Networks  0.321429             NaN      Zero Imputation   
 93   Neural Networks  0.107325             NaN      Zero Imputation   
 94   Neural Networks  0.609005             NaN      Zero Imputation   
 95   Neural Networks  0.222500             NaN      Zero Imputation   
 96   Neural Networks  0.218010             NaN      Zero Imputation   
 97   Neural Networks       NaN            relu      Zero Imputation   
 98   Neural Networks       NaN          0.0001      Zero Imputation   
 99   Neural Networks       NaN            auto      Zero Imputation   
 100  Neural Networks       NaN             0.9      Zero Imputation   
 101  Neural Networks       NaN           0.999      Zero Imputation   
 102  Neural Networks       NaN           False      Zero Imputation   
 103  Neural Networks       NaN             0.0      Zero Imputation   
 104  Neural Networks       NaN          (100,)      Zero Imputation   
 105  Neural Networks       NaN        constant      Zero Imputation   
 106  Neural Networks       NaN           0.001      Zero Imputation   
 107  Neural Networks       NaN           15000      Zero Imputation   
 108  Neural Networks       NaN             200      Zero Imputation   
 109  Neural Networks       NaN             0.9      Zero Imputation   
 110  Neural Networks       NaN              10      Zero Imputation   
 111  Neural Networks       NaN            True      Zero Imputation   
 112  Neural Networks       NaN             0.5      Zero Imputation   
 113  Neural Networks       NaN            None      Zero Imputation   
 114  Neural Networks       NaN            True      Zero Imputation   
 115  Neural Networks       NaN            adam      Zero Imputation   
 116  Neural Networks       NaN          0.0001      Zero Imputation   
 117  Neural Networks       NaN             0.1      Zero Imputation   
 118  Neural Networks       NaN           False      Zero Imputation   
 119  Neural Networks       NaN           False      Zero Imputation   
 120  Neural Networks  0.721286             NaN      Zero Imputation   
 121  Neural Networks  0.094412             NaN      Zero Imputation   
 122  Neural Networks  0.453704             NaN      Zero Imputation   
 123  Neural Networks  0.156300             NaN      Zero Imputation   
 124  Neural Networks  0.639874             NaN      Zero Imputation   
 125  Neural Networks  0.294920             NaN      Zero Imputation   
 126  Neural Networks  0.279747             NaN      Zero Imputation   
 127  Neural Networks       NaN            relu      Zero Imputation   
 128  Neural Networks       NaN          0.0001      Zero Imputation   
 129  Neural Networks       NaN            auto      Zero Imputation   
 130  Neural Networks       NaN             0.9      Zero Imputation   
 131  Neural Networks       NaN           0.999      Zero Imputation   
 132  Neural Networks       NaN           False      Zero Imputation   
 133  Neural Networks       NaN             0.0      Zero Imputation   
 134  Neural Networks       NaN          (100,)      Zero Imputation   
 135  Neural Networks       NaN        constant      Zero Imputation   
 136  Neural Networks       NaN           0.001      Zero Imputation   
 137  Neural Networks       NaN           15000      Zero Imputation   
 138  Neural Networks       NaN             200      Zero Imputation   
 139  Neural Networks       NaN             0.9      Zero Imputation   
 140  Neural Networks       NaN              10      Zero Imputation   
 141  Neural Networks       NaN            True      Zero Imputation   
 142  Neural Networks       NaN             0.5      Zero Imputation   
 143  Neural Networks       NaN            None      Zero Imputation   
 144  Neural Networks       NaN            True      Zero Imputation   
 145  Neural Networks       NaN            adam      Zero Imputation   
 146  Neural Networks       NaN          0.0001      Zero Imputation   
 147  Neural Networks       NaN             0.1      Zero Imputation   
 148  Neural Networks       NaN           False      Zero Imputation   
 149  Neural Networks       NaN           False      Zero Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 1    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 2    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 3    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 4    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 5    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 6    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 7    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 8    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 9    Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 10   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 11   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 12   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 13   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 14   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 15   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 16   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 17   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 18   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 19   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 20   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 21   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 22   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 23   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 24   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 25   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 26   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 27   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 28   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 29   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 30   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 31   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 32   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 33   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 34   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 35   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 36   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 37   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 38   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 39   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 40   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 41   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 42   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 43   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 44   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 45   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 46   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 47   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 48   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 49   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 50   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 51   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 52   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 53   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 54   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 55   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 56   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 57   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 58   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 59   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 60   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 61   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 62   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 63   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 64   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 65   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 66   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 67   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 68   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 69   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 70   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 71   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 72   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 73   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 74   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 75   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 76   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 77   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 78   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 79   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 80   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 81   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 82   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 83   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 84   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 85   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 86   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 87   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 88   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 89   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 90   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 91   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 92   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 93   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 94   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 95   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 96   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 97   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 98   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 99   Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 100  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 101  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 102  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 103  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 104  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 105  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 106  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 107  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 108  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 109  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 110  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 111  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 112  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 113  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 114  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 115  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 116  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 117  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 118  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 119  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 120  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 121  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 122  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 123  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 124  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 125  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 126  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 127  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 128  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 129  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 130  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 131  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 132  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 133  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 134  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 135  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 136  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 137  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 138  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 139  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 140  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 141  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 142  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 143  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 144  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 145  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 146  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 147  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 148  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  
 149  Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.533579             NaN      Mean Imputation   
 1    Neural Networks  0.082244             NaN      Mean Imputation   
 2    Neural Networks  0.637131             NaN      Mean Imputation   
 3    Neural Networks  0.145683             NaN      Mean Imputation   
 4    Neural Networks  0.604780             NaN      Mean Imputation   
 5    Neural Networks  0.183414             NaN      Mean Imputation   
 6    Neural Networks  0.209560             NaN      Mean Imputation   
 7    Neural Networks       NaN            relu      Mean Imputation   
 8    Neural Networks       NaN          0.0001      Mean Imputation   
 9    Neural Networks       NaN            auto      Mean Imputation   
 10   Neural Networks       NaN             0.9      Mean Imputation   
 11   Neural Networks       NaN           0.999      Mean Imputation   
 12   Neural Networks       NaN           False      Mean Imputation   
 13   Neural Networks       NaN             0.0      Mean Imputation   
 14   Neural Networks       NaN          (100,)      Mean Imputation   
 15   Neural Networks       NaN        constant      Mean Imputation   
 16   Neural Networks       NaN           0.001      Mean Imputation   
 17   Neural Networks       NaN           15000      Mean Imputation   
 18   Neural Networks       NaN             200      Mean Imputation   
 19   Neural Networks       NaN             0.9      Mean Imputation   
 20   Neural Networks       NaN              10      Mean Imputation   
 21   Neural Networks       NaN            True      Mean Imputation   
 22   Neural Networks       NaN             0.5      Mean Imputation   
 23   Neural Networks       NaN            None      Mean Imputation   
 24   Neural Networks       NaN            True      Mean Imputation   
 25   Neural Networks       NaN            adam      Mean Imputation   
 26   Neural Networks       NaN          0.0001      Mean Imputation   
 27   Neural Networks       NaN             0.1      Mean Imputation   
 28   Neural Networks       NaN           False      Mean Imputation   
 29   Neural Networks       NaN           False      Mean Imputation   
 30   Neural Networks  0.658151             NaN      Mean Imputation   
 31   Neural Networks  0.071104             NaN      Mean Imputation   
 32   Neural Networks  0.573171             NaN      Mean Imputation   
 33   Neural Networks  0.126514             NaN      Mean Imputation   
 34   Neural Networks  0.648593             NaN      Mean Imputation   
 35   Neural Networks  0.265184             NaN      Mean Imputation   
 36   Neural Networks  0.297186             NaN      Mean Imputation   
 37   Neural Networks       NaN            relu      Mean Imputation   
 38   Neural Networks       NaN          0.0001      Mean Imputation   
 39   Neural Networks       NaN            auto      Mean Imputation   
 40   Neural Networks       NaN             0.9      Mean Imputation   
 41   Neural Networks       NaN           0.999      Mean Imputation   
 42   Neural Networks       NaN           False      Mean Imputation   
 43   Neural Networks       NaN             0.0      Mean Imputation   
 44   Neural Networks       NaN          (100,)      Mean Imputation   
 45   Neural Networks       NaN        constant      Mean Imputation   
 46   Neural Networks       NaN           0.001      Mean Imputation   
 47   Neural Networks       NaN           15000      Mean Imputation   
 48   Neural Networks       NaN             200      Mean Imputation   
 49   Neural Networks       NaN             0.9      Mean Imputation   
 50   Neural Networks       NaN              10      Mean Imputation   
 51   Neural Networks       NaN            True      Mean Imputation   
 52   Neural Networks       NaN             0.5      Mean Imputation   
 53   Neural Networks       NaN            None      Mean Imputation   
 54   Neural Networks       NaN            True      Mean Imputation   
 55   Neural Networks       NaN            adam      Mean Imputation   
 56   Neural Networks       NaN          0.0001      Mean Imputation   
 57   Neural Networks       NaN             0.1      Mean Imputation   
 58   Neural Networks       NaN           False      Mean Imputation   
 59   Neural Networks       NaN           False      Mean Imputation   
 60   Neural Networks  0.610482             NaN      Mean Imputation   
 61   Neural Networks  0.069333             NaN      Mean Imputation   
 62   Neural Networks  0.556150             NaN      Mean Imputation   
 63   Neural Networks  0.123296             NaN      Mean Imputation   
 64   Neural Networks  0.625584             NaN      Mean Imputation   
 65   Neural Networks  0.222173             NaN      Mean Imputation   
 66   Neural Networks  0.251168             NaN      Mean Imputation   
 67   Neural Networks       NaN            relu      Mean Imputation   
 68   Neural Networks       NaN          0.0001      Mean Imputation   
 69   Neural Networks       NaN            auto      Mean Imputation   
 70   Neural Networks       NaN             0.9      Mean Imputation   
 71   Neural Networks       NaN           0.999      Mean Imputation   
 72   Neural Networks       NaN           False      Mean Imputation   
 73   Neural Networks       NaN             0.0      Mean Imputation   
 74   Neural Networks       NaN          (100,)      Mean Imputation   
 75   Neural Networks       NaN        constant      Mean Imputation   
 76   Neural Networks       NaN           0.001      Mean Imputation   
 77   Neural Networks       NaN           15000      Mean Imputation   
 78   Neural Networks       NaN             200      Mean Imputation   
 79   Neural Networks       NaN             0.9      Mean Imputation   
 80   Neural Networks       NaN              10      Mean Imputation   
 81   Neural Networks       NaN            True      Mean Imputation   
 82   Neural Networks       NaN             0.5      Mean Imputation   
 83   Neural Networks       NaN            None      Mean Imputation   
 84   Neural Networks       NaN            True      Mean Imputation   
 85   Neural Networks       NaN            adam      Mean Imputation   
 86   Neural Networks       NaN          0.0001      Mean Imputation   
 87   Neural Networks       NaN             0.1      Mean Imputation   
 88   Neural Networks       NaN           False      Mean Imputation   
 89   Neural Networks       NaN           False      Mean Imputation   
 90   Neural Networks  0.625132             NaN      Mean Imputation   
 91   Neural Networks  0.081229             NaN      Mean Imputation   
 92   Neural Networks  0.607143             NaN      Mean Imputation   
 93   Neural Networks  0.143287             NaN      Mean Imputation   
 94   Neural Networks  0.650699             NaN      Mean Imputation   
 95   Neural Networks  0.273724             NaN      Mean Imputation   
 96   Neural Networks  0.301397             NaN      Mean Imputation   
 97   Neural Networks       NaN            relu      Mean Imputation   
 98   Neural Networks       NaN          0.0001      Mean Imputation   
 99   Neural Networks       NaN            auto      Mean Imputation   
 100  Neural Networks       NaN             0.9      Mean Imputation   
 101  Neural Networks       NaN           0.999      Mean Imputation   
 102  Neural Networks       NaN           False      Mean Imputation   
 103  Neural Networks       NaN             0.0      Mean Imputation   
 104  Neural Networks       NaN          (100,)      Mean Imputation   
 105  Neural Networks       NaN        constant      Mean Imputation   
 106  Neural Networks       NaN           0.001      Mean Imputation   
 107  Neural Networks       NaN           15000      Mean Imputation   
 108  Neural Networks       NaN             200      Mean Imputation   
 109  Neural Networks       NaN             0.9      Mean Imputation   
 110  Neural Networks       NaN              10      Mean Imputation   
 111  Neural Networks       NaN            True      Mean Imputation   
 112  Neural Networks       NaN             0.5      Mean Imputation   
 113  Neural Networks       NaN            None      Mean Imputation   
 114  Neural Networks       NaN            True      Mean Imputation   
 115  Neural Networks       NaN            adam      Mean Imputation   
 116  Neural Networks       NaN          0.0001      Mean Imputation   
 117  Neural Networks       NaN             0.1      Mean Imputation   
 118  Neural Networks       NaN           False      Mean Imputation   
 119  Neural Networks       NaN           False      Mean Imputation   
 120  Neural Networks  0.655954             NaN      Mean Imputation   
 121  Neural Networks  0.092676             NaN      Mean Imputation   
 122  Neural Networks  0.574074             NaN      Mean Imputation   
 123  Neural Networks  0.159588             NaN      Mean Imputation   
 124  Neural Networks  0.652773             NaN      Mean Imputation   
 125  Neural Networks  0.249591             NaN      Mean Imputation   
 126  Neural Networks  0.305547             NaN      Mean Imputation   
 127  Neural Networks       NaN            relu      Mean Imputation   
 128  Neural Networks       NaN          0.0001      Mean Imputation   
 129  Neural Networks       NaN            auto      Mean Imputation   
 130  Neural Networks       NaN             0.9      Mean Imputation   
 131  Neural Networks       NaN           0.999      Mean Imputation   
 132  Neural Networks       NaN           False      Mean Imputation   
 133  Neural Networks       NaN             0.0      Mean Imputation   
 134  Neural Networks       NaN          (100,)      Mean Imputation   
 135  Neural Networks       NaN        constant      Mean Imputation   
 136  Neural Networks       NaN           0.001      Mean Imputation   
 137  Neural Networks       NaN           15000      Mean Imputation   
 138  Neural Networks       NaN             200      Mean Imputation   
 139  Neural Networks       NaN             0.9      Mean Imputation   
 140  Neural Networks       NaN              10      Mean Imputation   
 141  Neural Networks       NaN            True      Mean Imputation   
 142  Neural Networks       NaN             0.5      Mean Imputation   
 143  Neural Networks       NaN            None      Mean Imputation   
 144  Neural Networks       NaN            True      Mean Imputation   
 145  Neural Networks       NaN            adam      Mean Imputation   
 146  Neural Networks       NaN          0.0001      Mean Imputation   
 147  Neural Networks       NaN             0.1      Mean Imputation   
 148  Neural Networks       NaN           False      Mean Imputation   
 149  Neural Networks       NaN           False      Mean Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 1    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 2    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 3    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 4    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 5    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 6    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 7    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 8    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 9    Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 10   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 11   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 12   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 13   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 14   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 15   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 16   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 17   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 18   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 19   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 20   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 21   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 22   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 23   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 24   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 25   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 26   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 27   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 28   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 29   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 30   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 31   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 32   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 33   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 34   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 35   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 36   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 37   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 38   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 39   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 40   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 41   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 42   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 43   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 44   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 45   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 46   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 47   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 48   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 49   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 50   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 51   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 52   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 53   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 54   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 55   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 56   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 57   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 58   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 59   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 60   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 61   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 62   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 63   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 64   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 65   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 66   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 67   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 68   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 69   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 70   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 71   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 72   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 73   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 74   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 75   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 76   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 77   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 78   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 79   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 80   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 81   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 82   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 83   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 84   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 85   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 86   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 87   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 88   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 89   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 90   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 91   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 92   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 93   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 94   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 95   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 96   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 97   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 98   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 99   Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 100  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 101  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 102  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 103  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 104  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 105  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 106  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 107  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 108  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 109  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 110  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 111  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 112  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 113  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 114  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 115  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 116  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 117  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 118  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 119  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 120  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 121  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 122  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 123  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 124  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 125  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 126  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 127  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 128  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 129  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 130  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 131  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 132  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 133  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 134  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 135  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 136  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 137  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 138  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 139  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 140  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 141  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 142  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 143  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 144  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 145  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 146  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 147  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 148  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  
 149  Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.723729             NaN    Median Imputation   
 1    Neural Networks  0.098814             NaN    Median Imputation   
 2    Neural Networks  0.421941             NaN    Median Imputation   
 3    Neural Networks  0.160128             NaN    Median Imputation   
 4    Neural Networks  0.616115             NaN    Median Imputation   
 5    Neural Networks  0.192737             NaN    Median Imputation   
 6    Neural Networks  0.232230             NaN    Median Imputation   
 7    Neural Networks       NaN            relu    Median Imputation   
 8    Neural Networks       NaN          0.0001    Median Imputation   
 9    Neural Networks       NaN            auto    Median Imputation   
 10   Neural Networks       NaN             0.9    Median Imputation   
 11   Neural Networks       NaN           0.999    Median Imputation   
 12   Neural Networks       NaN           False    Median Imputation   
 13   Neural Networks       NaN             0.0    Median Imputation   
 14   Neural Networks       NaN          (100,)    Median Imputation   
 15   Neural Networks       NaN        constant    Median Imputation   
 16   Neural Networks       NaN           0.001    Median Imputation   
 17   Neural Networks       NaN           15000    Median Imputation   
 18   Neural Networks       NaN             200    Median Imputation   
 19   Neural Networks       NaN             0.9    Median Imputation   
 20   Neural Networks       NaN              10    Median Imputation   
 21   Neural Networks       NaN            True    Median Imputation   
 22   Neural Networks       NaN             0.5    Median Imputation   
 23   Neural Networks       NaN            None    Median Imputation   
 24   Neural Networks       NaN            True    Median Imputation   
 25   Neural Networks       NaN            adam    Median Imputation   
 26   Neural Networks       NaN          0.0001    Median Imputation   
 27   Neural Networks       NaN             0.1    Median Imputation   
 28   Neural Networks       NaN           False    Median Imputation   
 29   Neural Networks       NaN           False    Median Imputation   
 30   Neural Networks  0.600211             NaN    Median Imputation   
 31   Neural Networks  0.062661             NaN    Median Imputation   
 32   Neural Networks  0.591463             NaN    Median Imputation   
 33   Neural Networks  0.113318             NaN    Median Imputation   
 34   Neural Networks  0.624543             NaN    Median Imputation   
 35   Neural Networks  0.224880             NaN    Median Imputation   
 36   Neural Networks  0.249085             NaN    Median Imputation   
 37   Neural Networks       NaN            relu    Median Imputation   
 38   Neural Networks       NaN          0.0001    Median Imputation   
 39   Neural Networks       NaN            auto    Median Imputation   
 40   Neural Networks       NaN             0.9    Median Imputation   
 41   Neural Networks       NaN           0.999    Median Imputation   
 42   Neural Networks       NaN           False    Median Imputation   
 43   Neural Networks       NaN             0.0    Median Imputation   
 44   Neural Networks       NaN          (100,)    Median Imputation   
 45   Neural Networks       NaN        constant    Median Imputation   
 46   Neural Networks       NaN           0.001    Median Imputation   
 47   Neural Networks       NaN           15000    Median Imputation   
 48   Neural Networks       NaN             200    Median Imputation   
 49   Neural Networks       NaN             0.9    Median Imputation   
 50   Neural Networks       NaN              10    Median Imputation   
 51   Neural Networks       NaN            True    Median Imputation   
 52   Neural Networks       NaN             0.5    Median Imputation   
 53   Neural Networks       NaN            None    Median Imputation   
 54   Neural Networks       NaN            True    Median Imputation   
 55   Neural Networks       NaN            adam    Median Imputation   
 56   Neural Networks       NaN          0.0001    Median Imputation   
 57   Neural Networks       NaN             0.1    Median Imputation   
 58   Neural Networks       NaN           False    Median Imputation   
 59   Neural Networks       NaN           False    Median Imputation   
 60   Neural Networks  0.871477             NaN    Median Imputation   
 61   Neural Networks  0.094340             NaN    Median Imputation   
 62   Neural Networks  0.187166             NaN    Median Imputation   
 63   Neural Networks  0.125448             NaN    Median Imputation   
 64   Neural Networks  0.649687             NaN    Median Imputation   
 65   Neural Networks  0.286705             NaN    Median Imputation   
 66   Neural Networks  0.299373             NaN    Median Imputation   
 67   Neural Networks       NaN            relu    Median Imputation   
 68   Neural Networks       NaN          0.0001    Median Imputation   
 69   Neural Networks       NaN            auto    Median Imputation   
 70   Neural Networks       NaN             0.9    Median Imputation   
 71   Neural Networks       NaN           0.999    Median Imputation   
 72   Neural Networks       NaN           False    Median Imputation   
 73   Neural Networks       NaN             0.0    Median Imputation   
 74   Neural Networks       NaN          (100,)    Median Imputation   
 75   Neural Networks       NaN        constant    Median Imputation   
 76   Neural Networks       NaN           0.001    Median Imputation   
 77   Neural Networks       NaN           15000    Median Imputation   
 78   Neural Networks       NaN             200    Median Imputation   
 79   Neural Networks       NaN             0.9    Median Imputation   
 80   Neural Networks       NaN              10    Median Imputation   
 81   Neural Networks       NaN            True    Median Imputation   
 82   Neural Networks       NaN             0.5    Median Imputation   
 83   Neural Networks       NaN            None    Median Imputation   
 84   Neural Networks       NaN            True    Median Imputation   
 85   Neural Networks       NaN            adam    Median Imputation   
 86   Neural Networks       NaN          0.0001    Median Imputation   
 87   Neural Networks       NaN             0.1    Median Imputation   
 88   Neural Networks       NaN           False    Median Imputation   
 89   Neural Networks       NaN           False    Median Imputation   
 90   Neural Networks  0.537671             NaN    Median Imputation   
 91   Neural Networks  0.069575             NaN    Median Imputation   
 92   Neural Networks  0.642857             NaN    Median Imputation   
 93   Neural Networks  0.125561             NaN    Median Imputation   
 94   Neural Networks  0.608250             NaN    Median Imputation   
 95   Neural Networks  0.193254             NaN    Median Imputation   
 96   Neural Networks  0.216499             NaN    Median Imputation   
 97   Neural Networks       NaN            relu    Median Imputation   
 98   Neural Networks       NaN          0.0001    Median Imputation   
 99   Neural Networks       NaN            auto    Median Imputation   
 100  Neural Networks       NaN             0.9    Median Imputation   
 101  Neural Networks       NaN           0.999    Median Imputation   
 102  Neural Networks       NaN           False    Median Imputation   
 103  Neural Networks       NaN             0.0    Median Imputation   
 104  Neural Networks       NaN          (100,)    Median Imputation   
 105  Neural Networks       NaN        constant    Median Imputation   
 106  Neural Networks       NaN           0.001    Median Imputation   
 107  Neural Networks       NaN           15000    Median Imputation   
 108  Neural Networks       NaN             200    Median Imputation   
 109  Neural Networks       NaN             0.9    Median Imputation   
 110  Neural Networks       NaN              10    Median Imputation   
 111  Neural Networks       NaN            True    Median Imputation   
 112  Neural Networks       NaN             0.5    Median Imputation   
 113  Neural Networks       NaN            None    Median Imputation   
 114  Neural Networks       NaN            True    Median Imputation   
 115  Neural Networks       NaN            adam    Median Imputation   
 116  Neural Networks       NaN          0.0001    Median Imputation   
 117  Neural Networks       NaN             0.1    Median Imputation   
 118  Neural Networks       NaN           False    Median Imputation   
 119  Neural Networks       NaN           False    Median Imputation   
 120  Neural Networks  0.434668             NaN    Median Imputation   
 121  Neural Networks  0.068811             NaN    Median Imputation   
 122  Neural Networks  0.712963             NaN    Median Imputation   
 123  Neural Networks  0.125509             NaN    Median Imputation   
 124  Neural Networks  0.591387             NaN    Median Imputation   
 125  Neural Networks  0.160196             NaN    Median Imputation   
 126  Neural Networks  0.182775             NaN    Median Imputation   
 127  Neural Networks       NaN            relu    Median Imputation   
 128  Neural Networks       NaN          0.0001    Median Imputation   
 129  Neural Networks       NaN            auto    Median Imputation   
 130  Neural Networks       NaN             0.9    Median Imputation   
 131  Neural Networks       NaN           0.999    Median Imputation   
 132  Neural Networks       NaN           False    Median Imputation   
 133  Neural Networks       NaN             0.0    Median Imputation   
 134  Neural Networks       NaN          (100,)    Median Imputation   
 135  Neural Networks       NaN        constant    Median Imputation   
 136  Neural Networks       NaN           0.001    Median Imputation   
 137  Neural Networks       NaN           15000    Median Imputation   
 138  Neural Networks       NaN             200    Median Imputation   
 139  Neural Networks       NaN             0.9    Median Imputation   
 140  Neural Networks       NaN              10    Median Imputation   
 141  Neural Networks       NaN            True    Median Imputation   
 142  Neural Networks       NaN             0.5    Median Imputation   
 143  Neural Networks       NaN            None    Median Imputation   
 144  Neural Networks       NaN            True    Median Imputation   
 145  Neural Networks       NaN            adam    Median Imputation   
 146  Neural Networks       NaN          0.0001    Median Imputation   
 147  Neural Networks       NaN             0.1    Median Imputation   
 148  Neural Networks       NaN           False    Median Imputation   
 149  Neural Networks       NaN           False    Median Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 1    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 2    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 3    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 4    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 5    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 6    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 7    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 8    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 9    Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 10   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 11   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 12   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 13   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 14   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 15   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 16   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 17   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 18   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 19   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 20   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 21   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 22   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 23   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 24   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 25   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 26   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 27   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 28   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 29   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 30   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 31   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 32   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 33   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 34   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 35   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 36   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 37   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 38   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 39   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 40   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 41   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 42   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 43   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 44   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 45   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 46   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 47   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 48   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 49   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 50   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 51   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 52   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 53   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 54   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 55   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 56   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 57   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 58   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 59   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 60   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 61   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 62   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 63   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 64   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 65   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 66   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 67   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 68   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 69   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 70   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 71   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 72   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 73   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 74   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 75   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 76   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 77   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 78   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 79   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 80   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 81   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 82   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 83   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 84   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 85   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 86   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 87   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 88   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 89   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 90   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 91   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 92   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 93   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 94   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 95   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 96   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 97   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 98   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 99   Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 100  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 101  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 102  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 103  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 104  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 105  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 106  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 107  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 108  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 109  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 110  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 111  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 112  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 113  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 114  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 115  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 116  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 117  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 118  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 119  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 120  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 121  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 122  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 123  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 124  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 125  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 126  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 127  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 128  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 129  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 130  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 131  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 132  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 133  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 134  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 135  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 136  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 137  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 138  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 139  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 140  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 141  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 142  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 143  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 144  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 145  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 146  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 147  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 148  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  
 149  Neural Networks_Hybrid Sampling (SMOTEENN)_Med...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.750329             NaN      Zero Imputation   
 1    Neural Networks  0.105438             NaN      Zero Imputation   
 2    Neural Networks  0.400844             NaN      Zero Imputation   
 3    Neural Networks  0.166960             NaN      Zero Imputation   
 4    Neural Networks  0.624751             NaN      Zero Imputation   
 5    Neural Networks  0.216705             NaN      Zero Imputation   
 6    Neural Networks  0.249501             NaN      Zero Imputation   
 7    Neural Networks       NaN            relu      Zero Imputation   
 8    Neural Networks       NaN          0.0001      Zero Imputation   
 9    Neural Networks       NaN            auto      Zero Imputation   
 10   Neural Networks       NaN             0.9      Zero Imputation   
 11   Neural Networks       NaN           0.999      Zero Imputation   
 12   Neural Networks       NaN           False      Zero Imputation   
 13   Neural Networks       NaN             0.0      Zero Imputation   
 14   Neural Networks       NaN          (100,)      Zero Imputation   
 15   Neural Networks       NaN        constant      Zero Imputation   
 16   Neural Networks       NaN           0.001      Zero Imputation   
 17   Neural Networks       NaN           15000      Zero Imputation   
 18   Neural Networks       NaN             200      Zero Imputation   
 19   Neural Networks       NaN             0.9      Zero Imputation   
 20   Neural Networks       NaN              10      Zero Imputation   
 21   Neural Networks       NaN            True      Zero Imputation   
 22   Neural Networks       NaN             0.5      Zero Imputation   
 23   Neural Networks       NaN            None      Zero Imputation   
 24   Neural Networks       NaN            True      Zero Imputation   
 25   Neural Networks       NaN            adam      Zero Imputation   
 26   Neural Networks       NaN          0.0001      Zero Imputation   
 27   Neural Networks       NaN             0.1      Zero Imputation   
 28   Neural Networks       NaN           False      Zero Imputation   
 29   Neural Networks       NaN           False      Zero Imputation   
 30   Neural Networks  0.776139             NaN      Zero Imputation   
 31   Neural Networks  0.087740             NaN      Zero Imputation   
 32   Neural Networks  0.445122             NaN      Zero Imputation   
 33   Neural Networks  0.146586             NaN      Zero Imputation   
 34   Neural Networks  0.631489             NaN      Zero Imputation   
 35   Neural Networks  0.244377             NaN      Zero Imputation   
 36   Neural Networks  0.262979             NaN      Zero Imputation   
 37   Neural Networks       NaN            relu      Zero Imputation   
 38   Neural Networks       NaN          0.0001      Zero Imputation   
 39   Neural Networks       NaN            auto      Zero Imputation   
 40   Neural Networks       NaN             0.9      Zero Imputation   
 41   Neural Networks       NaN           0.999      Zero Imputation   
 42   Neural Networks       NaN           False      Zero Imputation   
 43   Neural Networks       NaN             0.0      Zero Imputation   
 44   Neural Networks       NaN          (100,)      Zero Imputation   
 45   Neural Networks       NaN        constant      Zero Imputation   
 46   Neural Networks       NaN           0.001      Zero Imputation   
 47   Neural Networks       NaN           15000      Zero Imputation   
 48   Neural Networks       NaN             200      Zero Imputation   
 49   Neural Networks       NaN             0.9      Zero Imputation   
 50   Neural Networks       NaN              10      Zero Imputation   
 51   Neural Networks       NaN            True      Zero Imputation   
 52   Neural Networks       NaN             0.5      Zero Imputation   
 53   Neural Networks       NaN            None      Zero Imputation   
 54   Neural Networks       NaN            True      Zero Imputation   
 55   Neural Networks       NaN            adam      Zero Imputation   
 56   Neural Networks       NaN          0.0001      Zero Imputation   
 57   Neural Networks       NaN             0.1      Zero Imputation   
 58   Neural Networks       NaN           False      Zero Imputation   
 59   Neural Networks       NaN           False      Zero Imputation   
 60   Neural Networks  0.785093             NaN      Zero Imputation   
 61   Neural Networks  0.102402             NaN      Zero Imputation   
 62   Neural Networks  0.433155             NaN      Zero Imputation   
 63   Neural Networks  0.165644             NaN      Zero Imputation   
 64   Neural Networks  0.646584             NaN      Zero Imputation   
 65   Neural Networks  0.260207             NaN      Zero Imputation   
 66   Neural Networks  0.293168             NaN      Zero Imputation   
 67   Neural Networks       NaN            relu      Zero Imputation   
 68   Neural Networks       NaN          0.0001      Zero Imputation   
 69   Neural Networks       NaN            auto      Zero Imputation   
 70   Neural Networks       NaN             0.9      Zero Imputation   
 71   Neural Networks       NaN           0.999      Zero Imputation   
 72   Neural Networks       NaN           False      Zero Imputation   
 73   Neural Networks       NaN             0.0      Zero Imputation   
 74   Neural Networks       NaN          (100,)      Zero Imputation   
 75   Neural Networks       NaN        constant      Zero Imputation   
 76   Neural Networks       NaN           0.001      Zero Imputation   
 77   Neural Networks       NaN           15000      Zero Imputation   
 78   Neural Networks       NaN             200      Zero Imputation   
 79   Neural Networks       NaN             0.9      Zero Imputation   
 80   Neural Networks       NaN              10      Zero Imputation   
 81   Neural Networks       NaN            True      Zero Imputation   
 82   Neural Networks       NaN             0.5      Zero Imputation   
 83   Neural Networks       NaN            None      Zero Imputation   
 84   Neural Networks       NaN            True      Zero Imputation   
 85   Neural Networks       NaN            adam      Zero Imputation   
 86   Neural Networks       NaN          0.0001      Zero Imputation   
 87   Neural Networks       NaN             0.1      Zero Imputation   
 88   Neural Networks       NaN           False      Zero Imputation   
 89   Neural Networks       NaN           False      Zero Imputation   
 90   Neural Networks  0.801370             NaN      Zero Imputation   
 91   Neural Networks  0.086053             NaN      Zero Imputation   
 92   Neural Networks  0.295918             NaN      Zero Imputation   
 93   Neural Networks  0.133333             NaN      Zero Imputation   
 94   Neural Networks  0.621961             NaN      Zero Imputation   
 95   Neural Networks  0.225437             NaN      Zero Imputation   
 96   Neural Networks  0.243921             NaN      Zero Imputation   
 97   Neural Networks       NaN            relu      Zero Imputation   
 98   Neural Networks       NaN          0.0001      Zero Imputation   
 99   Neural Networks       NaN            auto      Zero Imputation   
 100  Neural Networks       NaN             0.9      Zero Imputation   
 101  Neural Networks       NaN           0.999      Zero Imputation   
 102  Neural Networks       NaN           False      Zero Imputation   
 103  Neural Networks       NaN             0.0      Zero Imputation   
 104  Neural Networks       NaN          (100,)      Zero Imputation   
 105  Neural Networks       NaN        constant      Zero Imputation   
 106  Neural Networks       NaN           0.001      Zero Imputation   
 107  Neural Networks       NaN           15000      Zero Imputation   
 108  Neural Networks       NaN             200      Zero Imputation   
 109  Neural Networks       NaN             0.9      Zero Imputation   
 110  Neural Networks       NaN              10      Zero Imputation   
 111  Neural Networks       NaN            True      Zero Imputation   
 112  Neural Networks       NaN             0.5      Zero Imputation   
 113  Neural Networks       NaN            None      Zero Imputation   
 114  Neural Networks       NaN            True      Zero Imputation   
 115  Neural Networks       NaN            adam      Zero Imputation   
 116  Neural Networks       NaN          0.0001      Zero Imputation   
 117  Neural Networks       NaN             0.1      Zero Imputation   
 118  Neural Networks       NaN           False      Zero Imputation   
 119  Neural Networks       NaN           False      Zero Imputation   
 120  Neural Networks  0.796365             NaN      Zero Imputation   
 121  Neural Networks  0.101574             NaN      Zero Imputation   
 122  Neural Networks  0.328704             NaN      Zero Imputation   
 123  Neural Networks  0.155191             NaN      Zero Imputation   
 124  Neural Networks  0.648106             NaN      Zero Imputation   
 125  Neural Networks  0.294082             NaN      Zero Imputation   
 126  Neural Networks  0.296212             NaN      Zero Imputation   
 127  Neural Networks       NaN            relu      Zero Imputation   
 128  Neural Networks       NaN          0.0001      Zero Imputation   
 129  Neural Networks       NaN            auto      Zero Imputation   
 130  Neural Networks       NaN             0.9      Zero Imputation   
 131  Neural Networks       NaN           0.999      Zero Imputation   
 132  Neural Networks       NaN           False      Zero Imputation   
 133  Neural Networks       NaN             0.0      Zero Imputation   
 134  Neural Networks       NaN          (100,)      Zero Imputation   
 135  Neural Networks       NaN        constant      Zero Imputation   
 136  Neural Networks       NaN           0.001      Zero Imputation   
 137  Neural Networks       NaN           15000      Zero Imputation   
 138  Neural Networks       NaN             200      Zero Imputation   
 139  Neural Networks       NaN             0.9      Zero Imputation   
 140  Neural Networks       NaN              10      Zero Imputation   
 141  Neural Networks       NaN            True      Zero Imputation   
 142  Neural Networks       NaN             0.5      Zero Imputation   
 143  Neural Networks       NaN            None      Zero Imputation   
 144  Neural Networks       NaN            True      Zero Imputation   
 145  Neural Networks       NaN            adam      Zero Imputation   
 146  Neural Networks       NaN          0.0001      Zero Imputation   
 147  Neural Networks       NaN             0.1      Zero Imputation   
 148  Neural Networks       NaN           False      Zero Imputation   
 149  Neural Networks       NaN           False      Zero Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 1    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 2    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 3    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 4    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 5    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 6    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 7    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 8    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 9    Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 10   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 11   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 12   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 13   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 14   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 15   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 16   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 17   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 18   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 19   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 20   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 21   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 22   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 23   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 24   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 25   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 26   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 27   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 28   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 29   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 30   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 31   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 32   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 33   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 34   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 35   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 36   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 37   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 38   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 39   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 40   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 41   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 42   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 43   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 44   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 45   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 46   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 47   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 48   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 49   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 50   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 51   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 52   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 53   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 54   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 55   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 56   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 57   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 58   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 59   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 60   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 61   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 62   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 63   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 64   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 65   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 66   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 67   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 68   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 69   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 70   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 71   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 72   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 73   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 74   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 75   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 76   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 77   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 78   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 79   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 80   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 81   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 82   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 83   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 84   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 85   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 86   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 87   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 88   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 89   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 90   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 91   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 92   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 93   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 94   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 95   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 96   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 97   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 98   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 99   Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 100  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 101  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 102  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 103  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 104  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 105  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 106  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 107  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 108  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 109  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 110  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 111  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 112  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 113  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 114  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 115  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 116  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 117  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 118  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 119  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 120  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 121  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 122  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 123  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 124  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 125  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 126  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 127  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 128  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 129  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 130  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 131  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 132  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 133  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 134  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 135  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 136  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 137  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 138  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 139  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 140  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 141  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 142  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 143  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 144  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 145  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 146  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 147  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 148  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  
 149  Neural Networks_Hybrid Sampling (SMOTETomek)_Z...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.825652             NaN      Mean Imputation   
 1    Neural Networks  0.112933             NaN      Mean Imputation   
 2    Neural Networks  0.261603             NaN      Mean Imputation   
 3    Neural Networks  0.157761             NaN      Mean Imputation   
 4    Neural Networks  0.637041             NaN      Mean Imputation   
 5    Neural Networks  0.248500             NaN      Mean Imputation   
 6    Neural Networks  0.274083             NaN      Mean Imputation   
 7    Neural Networks       NaN            relu      Mean Imputation   
 8    Neural Networks       NaN          0.0001      Mean Imputation   
 9    Neural Networks       NaN            auto      Mean Imputation   
 10   Neural Networks       NaN             0.9      Mean Imputation   
 11   Neural Networks       NaN           0.999      Mean Imputation   
 12   Neural Networks       NaN           False      Mean Imputation   
 13   Neural Networks       NaN             0.0      Mean Imputation   
 14   Neural Networks       NaN          (100,)      Mean Imputation   
 15   Neural Networks       NaN        constant      Mean Imputation   
 16   Neural Networks       NaN           0.001      Mean Imputation   
 17   Neural Networks       NaN           15000      Mean Imputation   
 18   Neural Networks       NaN             200      Mean Imputation   
 19   Neural Networks       NaN             0.9      Mean Imputation   
 20   Neural Networks       NaN              10      Mean Imputation   
 21   Neural Networks       NaN            True      Mean Imputation   
 22   Neural Networks       NaN             0.5      Mean Imputation   
 23   Neural Networks       NaN            None      Mean Imputation   
 24   Neural Networks       NaN            True      Mean Imputation   
 25   Neural Networks       NaN            adam      Mean Imputation   
 26   Neural Networks       NaN          0.0001      Mean Imputation   
 27   Neural Networks       NaN             0.1      Mean Imputation   
 28   Neural Networks       NaN           False      Mean Imputation   
 29   Neural Networks       NaN           False      Mean Imputation   
 30   Neural Networks  0.929681             NaN      Mean Imputation   
 31   Neural Networks  0.088000             NaN      Mean Imputation   
 32   Neural Networks  0.067073             NaN      Mean Imputation   
 33   Neural Networks  0.076125             NaN      Mean Imputation   
 34   Neural Networks  0.649786             NaN      Mean Imputation   
 35   Neural Networks  0.286332             NaN      Mean Imputation   
 36   Neural Networks  0.299571             NaN      Mean Imputation   
 37   Neural Networks       NaN            relu      Mean Imputation   
 38   Neural Networks       NaN          0.0001      Mean Imputation   
 39   Neural Networks       NaN            auto      Mean Imputation   
 40   Neural Networks       NaN             0.9      Mean Imputation   
 41   Neural Networks       NaN           0.999      Mean Imputation   
 42   Neural Networks       NaN           False      Mean Imputation   
 43   Neural Networks       NaN             0.0      Mean Imputation   
 44   Neural Networks       NaN          (100,)      Mean Imputation   
 45   Neural Networks       NaN        constant      Mean Imputation   
 46   Neural Networks       NaN           0.001      Mean Imputation   
 47   Neural Networks       NaN           15000      Mean Imputation   
 48   Neural Networks       NaN             200      Mean Imputation   
 49   Neural Networks       NaN             0.9      Mean Imputation   
 50   Neural Networks       NaN              10      Mean Imputation   
 51   Neural Networks       NaN            True      Mean Imputation   
 52   Neural Networks       NaN             0.5      Mean Imputation   
 53   Neural Networks       NaN            None      Mean Imputation   
 54   Neural Networks       NaN            True      Mean Imputation   
 55   Neural Networks       NaN            adam      Mean Imputation   
 56   Neural Networks       NaN          0.0001      Mean Imputation   
 57   Neural Networks       NaN             0.1      Mean Imputation   
 58   Neural Networks       NaN           False      Mean Imputation   
 59   Neural Networks       NaN           False      Mean Imputation   
 60   Neural Networks  0.676060             NaN      Mean Imputation   
 61   Neural Networks  0.077048             NaN      Mean Imputation   
 62   Neural Networks  0.508021             NaN      Mean Imputation   
 63   Neural Networks  0.133803             NaN      Mean Imputation   
 64   Neural Networks  0.645434             NaN      Mean Imputation   
 65   Neural Networks  0.237955             NaN      Mean Imputation   
 66   Neural Networks  0.290868             NaN      Mean Imputation   
 67   Neural Networks       NaN            relu      Mean Imputation   
 68   Neural Networks       NaN          0.0001      Mean Imputation   
 69   Neural Networks       NaN            auto      Mean Imputation   
 70   Neural Networks       NaN             0.9      Mean Imputation   
 71   Neural Networks       NaN           0.999      Mean Imputation   
 72   Neural Networks       NaN           False      Mean Imputation   
 73   Neural Networks       NaN             0.0      Mean Imputation   
 74   Neural Networks       NaN          (100,)      Mean Imputation   
 75   Neural Networks       NaN        constant      Mean Imputation   
 76   Neural Networks       NaN           0.001      Mean Imputation   
 77   Neural Networks       NaN           15000      Mean Imputation   
 78   Neural Networks       NaN             200      Mean Imputation   
 79   Neural Networks       NaN             0.9      Mean Imputation   
 80   Neural Networks       NaN              10      Mean Imputation   
 81   Neural Networks       NaN            True      Mean Imputation   
 82   Neural Networks       NaN             0.5      Mean Imputation   
 83   Neural Networks       NaN            None      Mean Imputation   
 84   Neural Networks       NaN            True      Mean Imputation   
 85   Neural Networks       NaN            adam      Mean Imputation   
 86   Neural Networks       NaN          0.0001      Mean Imputation   
 87   Neural Networks       NaN             0.1      Mean Imputation   
 88   Neural Networks       NaN           False      Mean Imputation   
 89   Neural Networks       NaN           False      Mean Imputation   
 90   Neural Networks  0.903056             NaN      Mean Imputation   
 91   Neural Networks  0.070000             NaN      Mean Imputation   
 92   Neural Networks  0.071429             NaN      Mean Imputation   
 93   Neural Networks  0.070707             NaN      Mean Imputation   
 94   Neural Networks  0.630675             NaN      Mean Imputation   
 95   Neural Networks  0.226967             NaN      Mean Imputation   
 96   Neural Networks  0.261351             NaN      Mean Imputation   
 97   Neural Networks       NaN            relu      Mean Imputation   
 98   Neural Networks       NaN          0.0001      Mean Imputation   
 99   Neural Networks       NaN            auto      Mean Imputation   
 100  Neural Networks       NaN             0.9      Mean Imputation   
 101  Neural Networks       NaN           0.999      Mean Imputation   
 102  Neural Networks       NaN           False      Mean Imputation   
 103  Neural Networks       NaN             0.0      Mean Imputation   
 104  Neural Networks       NaN          (100,)      Mean Imputation   
 105  Neural Networks       NaN        constant      Mean Imputation   
 106  Neural Networks       NaN           0.001      Mean Imputation   
 107  Neural Networks       NaN           15000      Mean Imputation   
 108  Neural Networks       NaN             200      Mean Imputation   
 109  Neural Networks       NaN             0.9      Mean Imputation   
 110  Neural Networks       NaN              10      Mean Imputation   
 111  Neural Networks       NaN            True      Mean Imputation   
 112  Neural Networks       NaN             0.5      Mean Imputation   
 113  Neural Networks       NaN            None      Mean Imputation   
 114  Neural Networks       NaN            True      Mean Imputation   
 115  Neural Networks       NaN            adam      Mean Imputation   
 116  Neural Networks       NaN          0.0001      Mean Imputation   
 117  Neural Networks       NaN             0.1      Mean Imputation   
 118  Neural Networks       NaN           False      Mean Imputation   
 119  Neural Networks       NaN           False      Mean Imputation   
 120  Neural Networks  0.712856             NaN      Mean Imputation   
 121  Neural Networks  0.094620             NaN      Mean Imputation   
 122  Neural Networks  0.472222             NaN      Mean Imputation   
 123  Neural Networks  0.157651             NaN      Mean Imputation   
 124  Neural Networks  0.633189             NaN      Mean Imputation   
 125  Neural Networks  0.210035             NaN      Mean Imputation   
 126  Neural Networks  0.266378             NaN      Mean Imputation   
 127  Neural Networks       NaN            relu      Mean Imputation   
 128  Neural Networks       NaN          0.0001      Mean Imputation   
 129  Neural Networks       NaN            auto      Mean Imputation   
 130  Neural Networks       NaN             0.9      Mean Imputation   
 131  Neural Networks       NaN           0.999      Mean Imputation   
 132  Neural Networks       NaN           False      Mean Imputation   
 133  Neural Networks       NaN             0.0      Mean Imputation   
 134  Neural Networks       NaN          (100,)      Mean Imputation   
 135  Neural Networks       NaN        constant      Mean Imputation   
 136  Neural Networks       NaN           0.001      Mean Imputation   
 137  Neural Networks       NaN           15000      Mean Imputation   
 138  Neural Networks       NaN             200      Mean Imputation   
 139  Neural Networks       NaN             0.9      Mean Imputation   
 140  Neural Networks       NaN              10      Mean Imputation   
 141  Neural Networks       NaN            True      Mean Imputation   
 142  Neural Networks       NaN             0.5      Mean Imputation   
 143  Neural Networks       NaN            None      Mean Imputation   
 144  Neural Networks       NaN            True      Mean Imputation   
 145  Neural Networks       NaN            adam      Mean Imputation   
 146  Neural Networks       NaN          0.0001      Mean Imputation   
 147  Neural Networks       NaN             0.1      Mean Imputation   
 148  Neural Networks       NaN           False      Mean Imputation   
 149  Neural Networks       NaN           False      Mean Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 1    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 2    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 3    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 4    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 5    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 6    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 7    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 8    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 9    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 10   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 11   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 12   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 13   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 14   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 15   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 16   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 17   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 18   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 19   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 20   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 21   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 22   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 23   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 24   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 25   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 26   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 27   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 28   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 29   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 30   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 31   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 32   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 33   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 34   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 35   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 36   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 37   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 38   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 39   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 40   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 41   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 42   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 43   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 44   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 45   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 46   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 47   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 48   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 49   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 50   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 51   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 52   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 53   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 54   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 55   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 56   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 57   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 58   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 59   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 60   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 61   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 62   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 63   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 64   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 65   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 66   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 67   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 68   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 69   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 70   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 71   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 72   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 73   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 74   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 75   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 76   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 77   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 78   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 79   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 80   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 81   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 82   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 83   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 84   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 85   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 86   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 87   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 88   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 89   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 90   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 91   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 92   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 93   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 94   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 95   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 96   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 97   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 98   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 99   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 100  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 101  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 102  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 103  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 104  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 105  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 106  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 107  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 108  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 109  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 110  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 111  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 112  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 113  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 114  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 115  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 116  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 117  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 118  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 119  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 120  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 121  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 122  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 123  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 124  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 125  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 126  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 127  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 128  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 129  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 130  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 131  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 132  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 133  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 134  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 135  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 136  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 137  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 138  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 139  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 140  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 141  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 142  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 143  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 144  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 145  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 146  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 147  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 148  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 149  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  ,
            Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Neural Networks  0.654991             NaN    Median Imputation   
 1    Neural Networks  0.089518             NaN    Median Imputation   
 2    Neural Networks  0.493671             NaN    Median Imputation   
 3    Neural Networks  0.151554             NaN    Median Imputation   
 4    Neural Networks  0.602210             NaN    Median Imputation   
 5    Neural Networks  0.189032             NaN    Median Imputation   
 6    Neural Networks  0.204421             NaN    Median Imputation   
 7    Neural Networks       NaN            relu    Median Imputation   
 8    Neural Networks       NaN          0.0001    Median Imputation   
 9    Neural Networks       NaN            auto    Median Imputation   
 10   Neural Networks       NaN             0.9    Median Imputation   
 11   Neural Networks       NaN           0.999    Median Imputation   
 12   Neural Networks       NaN           False    Median Imputation   
 13   Neural Networks       NaN             0.0    Median Imputation   
 14   Neural Networks       NaN          (100,)    Median Imputation   
 15   Neural Networks       NaN        constant    Median Imputation   
 16   Neural Networks       NaN           0.001    Median Imputation   
 17   Neural Networks       NaN           15000    Median Imputation   
 18   Neural Networks       NaN             200    Median Imputation   
 19   Neural Networks       NaN             0.9    Median Imputation   
 20   Neural Networks       NaN              10    Median Imputation   
 21   Neural Networks       NaN            True    Median Imputation   
 22   Neural Networks       NaN             0.5    Median Imputation   
 23   Neural Networks       NaN            None    Median Imputation   
 24   Neural Networks       NaN            True    Median Imputation   
 25   Neural Networks       NaN            adam    Median Imputation   
 26   Neural Networks       NaN          0.0001    Median Imputation   
 27   Neural Networks       NaN             0.1    Median Imputation   
 28   Neural Networks       NaN           False    Median Imputation   
 29   Neural Networks       NaN           False    Median Imputation   
 30   Neural Networks  0.666316             NaN    Median Imputation   
 31   Neural Networks  0.066090             NaN    Median Imputation   
 32   Neural Networks  0.512195             NaN    Median Imputation   
 33   Neural Networks  0.117073             NaN    Median Imputation   
 34   Neural Networks  0.620379             NaN    Median Imputation   
 35   Neural Networks  0.200919             NaN    Median Imputation   
 36   Neural Networks  0.240759             NaN    Median Imputation   
 37   Neural Networks       NaN            relu    Median Imputation   
 38   Neural Networks       NaN          0.0001    Median Imputation   
 39   Neural Networks       NaN            auto    Median Imputation   
 40   Neural Networks       NaN             0.9    Median Imputation   
 41   Neural Networks       NaN           0.999    Median Imputation   
 42   Neural Networks       NaN           False    Median Imputation   
 43   Neural Networks       NaN             0.0    Median Imputation   
 44   Neural Networks       NaN          (100,)    Median Imputation   
 45   Neural Networks       NaN        constant    Median Imputation   
 46   Neural Networks       NaN           0.001    Median Imputation   
 47   Neural Networks       NaN           15000    Median Imputation   
 48   Neural Networks       NaN             200    Median Imputation   
 49   Neural Networks       NaN             0.9    Median Imputation   
 50   Neural Networks       NaN              10    Median Imputation   
 51   Neural Networks       NaN            True    Median Imputation   
 52   Neural Networks       NaN             0.5    Median Imputation   
 53   Neural Networks       NaN            None    Median Imputation   
 54   Neural Networks       NaN            True    Median Imputation   
 55   Neural Networks       NaN            adam    Median Imputation   
 56   Neural Networks       NaN          0.0001    Median Imputation   
 57   Neural Networks       NaN             0.1    Median Imputation   
 58   Neural Networks       NaN           False    Median Imputation   
 59   Neural Networks       NaN           False    Median Imputation   
 60   Neural Networks  0.848828             NaN    Median Imputation   
 61   Neural Networks  0.094340             NaN    Median Imputation   
 62   Neural Networks  0.240642             NaN    Median Imputation   
 63   Neural Networks  0.135542             NaN    Median Imputation   
 64   Neural Networks  0.645083             NaN    Median Imputation   
 65   Neural Networks  0.271855             NaN    Median Imputation   
 66   Neural Networks  0.290167             NaN    Median Imputation   
 67   Neural Networks       NaN            relu    Median Imputation   
 68   Neural Networks       NaN          0.0001    Median Imputation   
 69   Neural Networks       NaN            auto    Median Imputation   
 70   Neural Networks       NaN             0.9    Median Imputation   
 71   Neural Networks       NaN           0.999    Median Imputation   
 72   Neural Networks       NaN           False    Median Imputation   
 73   Neural Networks       NaN             0.0    Median Imputation   
 74   Neural Networks       NaN          (100,)    Median Imputation   
 75   Neural Networks       NaN        constant    Median Imputation   
 76   Neural Networks       NaN           0.001    Median Imputation   
 77   Neural Networks       NaN           15000    Median Imputation   
 78   Neural Networks       NaN             200    Median Imputation   
 79   Neural Networks       NaN             0.9    Median Imputation   
 80   Neural Networks       NaN              10    Median Imputation   
 81   Neural Networks       NaN            True    Median Imputation   
 82   Neural Networks       NaN             0.5    Median Imputation   
 83   Neural Networks       NaN            None    Median Imputation   
 84   Neural Networks       NaN            True    Median Imputation   
 85   Neural Networks       NaN            adam    Median Imputation   
 86   Neural Networks       NaN          0.0001    Median Imputation   
 87   Neural Networks       NaN             0.1    Median Imputation   
 88   Neural Networks       NaN           False    Median Imputation   
 89   Neural Networks       NaN           False    Median Imputation   
 90   Neural Networks  0.834826             NaN    Median Imputation   
 91   Neural Networks  0.084778             NaN    Median Imputation   
 92   Neural Networks  0.224490             NaN    Median Imputation   
 93   Neural Networks  0.123077             NaN    Median Imputation   
 94   Neural Networks  0.628404             NaN    Median Imputation   
 95   Neural Networks  0.238390             NaN    Median Imputation   
 96   Neural Networks  0.256808             NaN    Median Imputation   
 97   Neural Networks       NaN            relu    Median Imputation   
 98   Neural Networks       NaN          0.0001    Median Imputation   
 99   Neural Networks       NaN            auto    Median Imputation   
 100  Neural Networks       NaN             0.9    Median Imputation   
 101  Neural Networks       NaN           0.999    Median Imputation   
 102  Neural Networks       NaN           False    Median Imputation   
 103  Neural Networks       NaN             0.0    Median Imputation   
 104  Neural Networks       NaN          (100,)    Median Imputation   
 105  Neural Networks       NaN        constant    Median Imputation   
 106  Neural Networks       NaN           0.001    Median Imputation   
 107  Neural Networks       NaN           15000    Median Imputation   
 108  Neural Networks       NaN             200    Median Imputation   
 109  Neural Networks       NaN             0.9    Median Imputation   
 110  Neural Networks       NaN              10    Median Imputation   
 111  Neural Networks       NaN            True    Median Imputation   
 112  Neural Networks       NaN             0.5    Median Imputation   
 113  Neural Networks       NaN            None    Median Imputation   
 114  Neural Networks       NaN            True    Median Imputation   
 115  Neural Networks       NaN            adam    Median Imputation   
 116  Neural Networks       NaN          0.0001    Median Imputation   
 117  Neural Networks       NaN             0.1    Median Imputation   
 118  Neural Networks       NaN           False    Median Imputation   
 119  Neural Networks       NaN           False    Median Imputation   
 120  Neural Networks  0.569810             NaN    Median Imputation   
 121  Neural Networks  0.075494             NaN    Median Imputation   
 122  Neural Networks  0.583333             NaN    Median Imputation   
 123  Neural Networks  0.133687             NaN    Median Imputation   
 124  Neural Networks  0.629997             NaN    Median Imputation   
 125  Neural Networks  0.204702             NaN    Median Imputation   
 126  Neural Networks  0.259994             NaN    Median Imputation   
 127  Neural Networks       NaN            relu    Median Imputation   
 128  Neural Networks       NaN          0.0001    Median Imputation   
 129  Neural Networks       NaN            auto    Median Imputation   
 130  Neural Networks       NaN             0.9    Median Imputation   
 131  Neural Networks       NaN           0.999    Median Imputation   
 132  Neural Networks       NaN           False    Median Imputation   
 133  Neural Networks       NaN             0.0    Median Imputation   
 134  Neural Networks       NaN          (100,)    Median Imputation   
 135  Neural Networks       NaN        constant    Median Imputation   
 136  Neural Networks       NaN           0.001    Median Imputation   
 137  Neural Networks       NaN           15000    Median Imputation   
 138  Neural Networks       NaN             200    Median Imputation   
 139  Neural Networks       NaN             0.9    Median Imputation   
 140  Neural Networks       NaN              10    Median Imputation   
 141  Neural Networks       NaN            True    Median Imputation   
 142  Neural Networks       NaN             0.5    Median Imputation   
 143  Neural Networks       NaN            None    Median Imputation   
 144  Neural Networks       NaN            True    Median Imputation   
 145  Neural Networks       NaN            adam    Median Imputation   
 146  Neural Networks       NaN          0.0001    Median Imputation   
 147  Neural Networks       NaN             0.1    Median Imputation   
 148  Neural Networks       NaN           False    Median Imputation   
 149  Neural Networks       NaN           False    Median Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 1    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 2    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 3    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 4    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 5    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 6    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 7    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 8    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 9    Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 10   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 11   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 12   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 13   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 14   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 15   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 16   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 17   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 18   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 19   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 20   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 21   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 22   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 23   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 24   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 25   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 26   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 27   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 28   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 29   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 30   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 31   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 32   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 33   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 34   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 35   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 36   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 37   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 38   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 39   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 40   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 41   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 42   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 43   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 44   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 45   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 46   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 47   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 48   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 49   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 50   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 51   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 52   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 53   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 54   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 55   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 56   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 57   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 58   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 59   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 60   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 61   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 62   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 63   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 64   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 65   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 66   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 67   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 68   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 69   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 70   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 71   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 72   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 73   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 74   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 75   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 76   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 77   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 78   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 79   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 80   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 81   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 82   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 83   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 84   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 85   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 86   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 87   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 88   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 89   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 90   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 91   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 92   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 93   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 94   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 95   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 96   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 97   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 98   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 99   Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 100  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 101  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 102  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 103  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 104  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 105  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 106  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 107  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 108  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 109  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 110  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 111  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 112  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 113  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 114  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 115  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 116  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 117  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 118  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 119  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 120  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 121  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 122  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 123  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 124  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 125  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 126  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 127  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 128  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 129  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 130  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 131  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 132  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 133  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 134  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 135  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 136  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 137  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 138  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 139  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 140  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 141  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 142  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 143  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 144  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 145  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 146  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 147  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 148  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  
 149  Neural Networks_Hybrid Sampling (SMOTETomek)_M...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.223334              NaN      Zero Imputation   
 1   Naive Bayes  0.065663              NaN      Zero Imputation   
 2   Naive Bayes  0.864979              NaN      Zero Imputation   
 3   Naive Bayes  0.122060              NaN      Zero Imputation   
 4   Naive Bayes  0.525013              NaN      Zero Imputation   
 5   Naive Bayes  0.055428              NaN      Zero Imputation   
 6   Naive Bayes  0.050026              NaN      Zero Imputation   
 7   Naive Bayes       NaN              NaN      Zero Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 9   Naive Bayes  0.208059              NaN      Zero Imputation   
 10  Naive Bayes  0.045411              NaN      Zero Imputation   
 11  Naive Bayes  0.865854              NaN      Zero Imputation   
 12  Naive Bayes  0.086296              NaN      Zero Imputation   
 13  Naive Bayes  0.523033              NaN      Zero Imputation   
 14  Naive Bayes  0.058868              NaN      Zero Imputation   
 15  Naive Bayes  0.046067              NaN      Zero Imputation   
 16  Naive Bayes       NaN              NaN      Zero Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 18  Naive Bayes  0.214643              NaN      Zero Imputation   
 19  Naive Bayes  0.055644              NaN      Zero Imputation   
 20  Naive Bayes  0.935829              NaN      Zero Imputation   
 21  Naive Bayes  0.105042              NaN      Zero Imputation   
 22  Naive Bayes  0.561959              NaN      Zero Imputation   
 23  Naive Bayes  0.124472              NaN      Zero Imputation   
 24  Naive Bayes  0.123918              NaN      Zero Imputation   
 25  Naive Bayes       NaN              NaN      Zero Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 27  Naive Bayes  0.206797              NaN      Zero Imputation   
 28  Naive Bayes  0.055292              NaN      Zero Imputation   
 29  Naive Bayes  0.892857              NaN      Zero Imputation   
 30  Naive Bayes  0.104136              NaN      Zero Imputation   
 31  Naive Bayes  0.534178              NaN      Zero Imputation   
 32  Naive Bayes  0.070635              NaN      Zero Imputation   
 33  Naive Bayes  0.068356              NaN      Zero Imputation   
 34  Naive Bayes       NaN              NaN      Zero Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 36  Naive Bayes  0.212065              NaN      Zero Imputation   
 37  Naive Bayes  0.063816              NaN      Zero Imputation   
 38  Naive Bayes  0.939815              NaN      Zero Imputation   
 39  Naive Bayes  0.119517              NaN      Zero Imputation   
 40  Naive Bayes  0.557759              NaN      Zero Imputation   
 41  Naive Bayes  0.117112              NaN      Zero Imputation   
 42  Naive Bayes  0.115518              NaN      Zero Imputation   
 43  Naive Bayes       NaN              NaN      Zero Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 
    Imbalance Class Technique                                Model Unique Code  
 0               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 1               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 2               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 3               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 4               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 5               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 6               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 7               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 8               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_0  
 9               Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 10              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 11              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 12              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 13              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 14              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 15              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 16              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 17              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_1  
 18              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 19              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 20              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 21              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 22              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 23              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 24              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 25              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 26              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_2  
 27              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 28              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 29              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 30              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 31              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 32              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 33              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 34              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 35              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_3  
 36              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 37              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 38              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 39              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 40              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 41              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 42              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 43              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  
 44              Oversampling  Naive Bayes_Oversampling_Zero Imputation_fold_4  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.214116              NaN      Mean Imputation   
 1   Naive Bayes  0.065211              NaN      Mean Imputation   
 2   Naive Bayes  0.869198              NaN      Mean Imputation   
 3   Naive Bayes  0.121319              NaN      Mean Imputation   
 4   Naive Bayes  0.520069              NaN      Mean Imputation   
 5   Naive Bayes  0.048693              NaN      Mean Imputation   
 6   Naive Bayes  0.040139              NaN      Mean Imputation   
 7   Naive Bayes       NaN              NaN      Mean Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 9   Naive Bayes  0.200948              NaN      Mean Imputation   
 10  Naive Bayes  0.044444              NaN      Mean Imputation   
 11  Naive Bayes  0.853659              NaN      Mean Imputation   
 12  Naive Bayes  0.084490              NaN      Mean Imputation   
 13  Naive Bayes  0.518148              NaN      Mean Imputation   
 14  Naive Bayes  0.061345              NaN      Mean Imputation   
 15  Naive Bayes  0.036297              NaN      Mean Imputation   
 16  Naive Bayes       NaN              NaN      Mean Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 18  Naive Bayes  0.205162              NaN      Mean Imputation   
 19  Naive Bayes  0.055294              NaN      Mean Imputation   
 20  Naive Bayes  0.941176              NaN      Mean Imputation   
 21  Naive Bayes  0.104451              NaN      Mean Imputation   
 22  Naive Bayes  0.553100              NaN      Mean Imputation   
 23  Naive Bayes  0.110706              NaN      Mean Imputation   
 24  Naive Bayes  0.106201              NaN      Mean Imputation   
 25  Naive Bayes       NaN              NaN      Mean Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 27  Naive Bayes  0.192571              NaN      Mean Imputation   
 28  Naive Bayes  0.054641              NaN      Mean Imputation   
 29  Naive Bayes  0.897959              NaN      Mean Imputation   
 30  Naive Bayes  0.103014              NaN      Mean Imputation   
 31  Naive Bayes  0.529030              NaN      Mean Imputation   
 32  Naive Bayes  0.061293              NaN      Mean Imputation   
 33  Naive Bayes  0.058060              NaN      Mean Imputation   
 34  Naive Bayes       NaN              NaN      Mean Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 36  Naive Bayes  0.202318              NaN      Mean Imputation   
 37  Naive Bayes  0.063896              NaN      Mean Imputation   
 38  Naive Bayes  0.953704              NaN      Mean Imputation   
 39  Naive Bayes  0.119767              NaN      Mean Imputation   
 40  Naive Bayes  0.559854              NaN      Mean Imputation   
 41  Naive Bayes  0.117789              NaN      Mean Imputation   
 42  Naive Bayes  0.119708              NaN      Mean Imputation   
 43  Naive Bayes       NaN              NaN      Mean Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 
    Imbalance Class Technique                                Model Unique Code  
 0               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 1               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 2               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 3               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 4               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 5               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 6               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 7               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 8               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_0  
 9               Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 10              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 11              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 12              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 13              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 14              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 15              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 16              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 17              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_1  
 18              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 19              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 20              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 21              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 22              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 23              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 24              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 25              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 26              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_2  
 27              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 28              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 29              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 30              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 31              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 32              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 33              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 34              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 35              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_3  
 36              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 37              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 38              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 39              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 40              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 41              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 42              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 43              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  
 44              Oversampling  Naive Bayes_Oversampling_Mean Imputation_fold_4  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.217277              NaN    Median Imputation   
 1   Naive Bayes  0.065183              NaN    Median Imputation   
 2   Naive Bayes  0.864979              NaN    Median Imputation   
 3   Naive Bayes  0.121230              NaN    Median Imputation   
 4   Naive Bayes  0.523200              NaN    Median Imputation   
 5   Naive Bayes  0.050934              NaN    Median Imputation   
 6   Naive Bayes  0.046399              NaN    Median Imputation   
 7   Naive Bayes       NaN              NaN    Median Imputation   
 8   Naive Bayes       NaN     1.000000e-09    Median Imputation   
 9   Naive Bayes  0.211219              NaN    Median Imputation   
 10  Naive Bayes  0.045002              NaN    Median Imputation   
 11  Naive Bayes  0.853659              NaN    Median Imputation   
 12  Naive Bayes  0.085496              NaN    Median Imputation   
 13  Naive Bayes  0.524629              NaN    Median Imputation   
 14  Naive Bayes  0.055606              NaN    Median Imputation   
 15  Naive Bayes  0.049257              NaN    Median Imputation   
 16  Naive Bayes       NaN              NaN    Median Imputation   
 17  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 18  Naive Bayes  0.212536              NaN    Median Imputation   
 19  Naive Bayes  0.055503              NaN    Median Imputation   
 20  Naive Bayes  0.935829              NaN    Median Imputation   
 21  Naive Bayes  0.104790              NaN    Median Imputation   
 22  Naive Bayes  0.559830              NaN    Median Imputation   
 23  Naive Bayes  0.120039              NaN    Median Imputation   
 24  Naive Bayes  0.119660              NaN    Median Imputation   
 25  Naive Bayes       NaN              NaN    Median Imputation   
 26  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 27  Naive Bayes  0.203635              NaN    Median Imputation   
 28  Naive Bayes  0.055083              NaN    Median Imputation   
 29  Naive Bayes  0.892857              NaN    Median Imputation   
 30  Naive Bayes  0.103765              NaN    Median Imputation   
 31  Naive Bayes  0.533959              NaN    Median Imputation   
 32  Naive Bayes  0.069246              NaN    Median Imputation   
 33  Naive Bayes  0.067918              NaN    Median Imputation   
 34  Naive Bayes       NaN              NaN    Median Imputation   
 35  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 36  Naive Bayes  0.208377              NaN    Median Imputation   
 37  Naive Bayes  0.064083              NaN    Median Imputation   
 38  Naive Bayes  0.949074              NaN    Median Imputation   
 39  Naive Bayes  0.120059              NaN    Median Imputation   
 40  Naive Bayes  0.559402              NaN    Median Imputation   
 41  Naive Bayes  0.120303              NaN    Median Imputation   
 42  Naive Bayes  0.118804              NaN    Median Imputation   
 43  Naive Bayes       NaN              NaN    Median Imputation   
 44  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 
                                     Model Unique Code  
 0   Naive Bayes_Oversampling_Median Imputation_fold_0  
 1   Naive Bayes_Oversampling_Median Imputation_fold_0  
 2   Naive Bayes_Oversampling_Median Imputation_fold_0  
 3   Naive Bayes_Oversampling_Median Imputation_fold_0  
 4   Naive Bayes_Oversampling_Median Imputation_fold_0  
 5   Naive Bayes_Oversampling_Median Imputation_fold_0  
 6   Naive Bayes_Oversampling_Median Imputation_fold_0  
 7   Naive Bayes_Oversampling_Median Imputation_fold_0  
 8   Naive Bayes_Oversampling_Median Imputation_fold_0  
 9   Naive Bayes_Oversampling_Median Imputation_fold_1  
 10  Naive Bayes_Oversampling_Median Imputation_fold_1  
 11  Naive Bayes_Oversampling_Median Imputation_fold_1  
 12  Naive Bayes_Oversampling_Median Imputation_fold_1  
 13  Naive Bayes_Oversampling_Median Imputation_fold_1  
 14  Naive Bayes_Oversampling_Median Imputation_fold_1  
 15  Naive Bayes_Oversampling_Median Imputation_fold_1  
 16  Naive Bayes_Oversampling_Median Imputation_fold_1  
 17  Naive Bayes_Oversampling_Median Imputation_fold_1  
 18  Naive Bayes_Oversampling_Median Imputation_fold_2  
 19  Naive Bayes_Oversampling_Median Imputation_fold_2  
 20  Naive Bayes_Oversampling_Median Imputation_fold_2  
 21  Naive Bayes_Oversampling_Median Imputation_fold_2  
 22  Naive Bayes_Oversampling_Median Imputation_fold_2  
 23  Naive Bayes_Oversampling_Median Imputation_fold_2  
 24  Naive Bayes_Oversampling_Median Imputation_fold_2  
 25  Naive Bayes_Oversampling_Median Imputation_fold_2  
 26  Naive Bayes_Oversampling_Median Imputation_fold_2  
 27  Naive Bayes_Oversampling_Median Imputation_fold_3  
 28  Naive Bayes_Oversampling_Median Imputation_fold_3  
 29  Naive Bayes_Oversampling_Median Imputation_fold_3  
 30  Naive Bayes_Oversampling_Median Imputation_fold_3  
 31  Naive Bayes_Oversampling_Median Imputation_fold_3  
 32  Naive Bayes_Oversampling_Median Imputation_fold_3  
 33  Naive Bayes_Oversampling_Median Imputation_fold_3  
 34  Naive Bayes_Oversampling_Median Imputation_fold_3  
 35  Naive Bayes_Oversampling_Median Imputation_fold_3  
 36  Naive Bayes_Oversampling_Median Imputation_fold_4  
 37  Naive Bayes_Oversampling_Median Imputation_fold_4  
 38  Naive Bayes_Oversampling_Median Imputation_fold_4  
 39  Naive Bayes_Oversampling_Median Imputation_fold_4  
 40  Naive Bayes_Oversampling_Median Imputation_fold_4  
 41  Naive Bayes_Oversampling_Median Imputation_fold_4  
 42  Naive Bayes_Oversampling_Median Imputation_fold_4  
 43  Naive Bayes_Oversampling_Median Imputation_fold_4  
 44  Naive Bayes_Oversampling_Median Imputation_fold_4  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.229655              NaN      Zero Imputation   
 1   Naive Bayes  0.066452              NaN      Zero Imputation   
 2   Naive Bayes  0.869198              NaN      Zero Imputation   
 3   Naive Bayes  0.123464              NaN      Zero Imputation   
 4   Naive Bayes  0.532896              NaN      Zero Imputation   
 5   Naive Bayes  0.072844              NaN      Zero Imputation   
 6   Naive Bayes  0.065792              NaN      Zero Imputation   
 7   Naive Bayes       NaN              NaN      Zero Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 9   Naive Bayes  0.254938              NaN      Zero Imputation   
 10  Naive Bayes  0.047538              NaN      Zero Imputation   
 11  Naive Bayes  0.853659              NaN      Zero Imputation   
 12  Naive Bayes  0.090061              NaN      Zero Imputation   
 13  Naive Bayes  0.548922              NaN      Zero Imputation   
 14  Naive Bayes  0.109053              NaN      Zero Imputation   
 15  Naive Bayes  0.097845              NaN      Zero Imputation   
 16  Naive Bayes       NaN              NaN      Zero Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 18  Naive Bayes  0.245457              NaN      Zero Imputation   
 19  Naive Bayes  0.056329              NaN      Zero Imputation   
 20  Naive Bayes  0.909091              NaN      Zero Imputation   
 21  Naive Bayes  0.106084              NaN      Zero Imputation   
 22  Naive Bayes  0.569309              NaN      Zero Imputation   
 23  Naive Bayes  0.138923              NaN      Zero Imputation   
 24  Naive Bayes  0.138618              NaN      Zero Imputation   
 25  Naive Bayes       NaN              NaN      Zero Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 27  Naive Bayes  0.228398              NaN      Zero Imputation   
 28  Naive Bayes  0.057050              NaN      Zero Imputation   
 29  Naive Bayes  0.897959              NaN      Zero Imputation   
 30  Naive Bayes  0.107284              NaN      Zero Imputation   
 31  Naive Bayes  0.551027              NaN      Zero Imputation   
 32  Naive Bayes  0.111015              NaN      Zero Imputation   
 33  Naive Bayes  0.102055              NaN      Zero Imputation   
 34  Naive Bayes       NaN              NaN      Zero Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 36  Naive Bayes  0.245522              NaN      Zero Imputation   
 37  Naive Bayes  0.065617              NaN      Zero Imputation   
 38  Naive Bayes  0.925926              NaN      Zero Imputation   
 39  Naive Bayes  0.122549              NaN      Zero Imputation   
 40  Naive Bayes  0.569207              NaN      Zero Imputation   
 41  Naive Bayes  0.140451              NaN      Zero Imputation   
 42  Naive Bayes  0.138414              NaN      Zero Imputation   
 43  Naive Bayes       NaN              NaN      Zero Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 
    Imbalance Class Technique                                 Model Unique Code  
 0              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 1              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 2              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 3              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 4              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 5              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 6              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 7              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 8              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_0  
 9              Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 10             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 11             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 12             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 13             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 14             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 15             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 16             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 17             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_1  
 18             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 19             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 20             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 21             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 22             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 23             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 24             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 25             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 26             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_2  
 27             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 28             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 29             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 30             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 31             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 32             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 33             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 34             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 35             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_3  
 36             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 37             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 38             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 39             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 40             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 41             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 42             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 43             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  
 44             Undersampling  Naive Bayes_Undersampling_Zero Imputation_fold_4  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.246510              NaN      Mean Imputation   
 1   Naive Bayes  0.066997              NaN      Mean Imputation   
 2   Naive Bayes  0.856540              NaN      Mean Imputation   
 3   Naive Bayes  0.124273              NaN      Mean Imputation   
 4   Naive Bayes  0.537565              NaN      Mean Imputation   
 5   Naive Bayes  0.079831              NaN      Mean Imputation   
 6   Naive Bayes  0.075130              NaN      Mean Imputation   
 7   Naive Bayes       NaN              NaN      Mean Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 9   Naive Bayes  0.209376              NaN      Mean Imputation   
 10  Naive Bayes  0.045192              NaN      Mean Imputation   
 11  Naive Bayes  0.859756              NaN      Mean Imputation   
 12  Naive Bayes  0.085871              NaN      Mean Imputation   
 13  Naive Bayes  0.528474              NaN      Mean Imputation   
 14  Naive Bayes  0.057389              NaN      Mean Imputation   
 15  Naive Bayes  0.056947              NaN      Mean Imputation   
 16  Naive Bayes       NaN              NaN      Mean Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 18  Naive Bayes  0.213853              NaN      Mean Imputation   
 19  Naive Bayes  0.055591              NaN      Mean Imputation   
 20  Naive Bayes  0.935829              NaN      Mean Imputation   
 21  Naive Bayes  0.104948              NaN      Mean Imputation   
 22  Naive Bayes  0.557655              NaN      Mean Imputation   
 23  Naive Bayes  0.117077              NaN      Mean Imputation   
 24  Naive Bayes  0.115310              NaN      Mean Imputation   
 25  Naive Bayes       NaN              NaN      Mean Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 27  Naive Bayes  0.190991              NaN      Mean Imputation   
 28  Naive Bayes  0.055367              NaN      Mean Imputation   
 29  Naive Bayes  0.913265              NaN      Mean Imputation   
 30  Naive Bayes  0.104404              NaN      Mean Imputation   
 31  Naive Bayes  0.536050              NaN      Mean Imputation   
 32  Naive Bayes  0.073163              NaN      Mean Imputation   
 33  Naive Bayes  0.072100              NaN      Mean Imputation   
 34  Naive Bayes       NaN              NaN      Mean Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 36  Naive Bayes  0.234194              NaN      Mean Imputation   
 37  Naive Bayes  0.064985              NaN      Mean Imputation   
 38  Naive Bayes  0.930556              NaN      Mean Imputation   
 39  Naive Bayes  0.121487              NaN      Mean Imputation   
 40  Naive Bayes  0.564278              NaN      Mean Imputation   
 41  Naive Bayes  0.127566              NaN      Mean Imputation   
 42  Naive Bayes  0.128556              NaN      Mean Imputation   
 43  Naive Bayes       NaN              NaN      Mean Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 
    Imbalance Class Technique                                 Model Unique Code  
 0              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 1              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 2              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 3              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 4              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 5              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 6              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 7              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 8              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_0  
 9              Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 10             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 11             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 12             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 13             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 14             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 15             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 16             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 17             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_1  
 18             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 19             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 20             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 21             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 22             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 23             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 24             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 25             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 26             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_2  
 27             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 28             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 29             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 30             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 31             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 32             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 33             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 34             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 35             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_3  
 36             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 37             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 38             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 39             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 40             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 41             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 42             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 43             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  
 44             Undersampling  Naive Bayes_Undersampling_Mean Imputation_fold_4  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.247827              NaN    Median Imputation   
 1   Naive Bayes  0.068534              NaN    Median Imputation   
 2   Naive Bayes  0.877637              NaN    Median Imputation   
 3   Naive Bayes  0.127139              NaN    Median Imputation   
 4   Naive Bayes  0.549276              NaN    Median Imputation   
 5   Naive Bayes  0.104042              NaN    Median Imputation   
 6   Naive Bayes  0.098552              NaN    Median Imputation   
 7   Naive Bayes       NaN              NaN    Median Imputation   
 8   Naive Bayes       NaN     1.000000e-09    Median Imputation   
 9   Naive Bayes  0.227811              NaN    Median Imputation   
 10  Naive Bayes  0.045634              NaN    Median Imputation   
 11  Naive Bayes  0.847561              NaN    Median Imputation   
 12  Naive Bayes  0.086604              NaN    Median Imputation   
 13  Naive Bayes  0.528625              NaN    Median Imputation   
 14  Naive Bayes  0.059698              NaN    Median Imputation   
 15  Naive Bayes  0.057250              NaN    Median Imputation   
 16  Naive Bayes       NaN              NaN    Median Imputation   
 17  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 18  Naive Bayes  0.274691              NaN    Median Imputation   
 19  Naive Bayes  0.056955              NaN    Median Imputation   
 20  Naive Bayes  0.882353              NaN    Median Imputation   
 21  Naive Bayes  0.107004              NaN    Median Imputation   
 22  Naive Bayes  0.576260              NaN    Median Imputation   
 23  Naive Bayes  0.150183              NaN    Median Imputation   
 24  Naive Bayes  0.152520              NaN    Median Imputation   
 25  Naive Bayes       NaN              NaN    Median Imputation   
 26  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 27  Naive Bayes  0.207850              NaN    Median Imputation   
 28  Naive Bayes  0.055643              NaN    Median Imputation   
 29  Naive Bayes  0.897959              NaN    Median Imputation   
 30  Naive Bayes  0.104793              NaN    Median Imputation   
 31  Naive Bayes  0.539891              NaN    Median Imputation   
 32  Naive Bayes  0.083237              NaN    Median Imputation   
 33  Naive Bayes  0.079782              NaN    Median Imputation   
 34  Naive Bayes       NaN              NaN    Median Imputation   
 35  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 36  Naive Bayes  0.233140              NaN    Median Imputation   
 37  Naive Bayes  0.064339              NaN    Median Imputation   
 38  Naive Bayes  0.921296              NaN    Median Imputation   
 39  Naive Bayes  0.120278              NaN    Median Imputation   
 40  Naive Bayes  0.566532              NaN    Median Imputation   
 41  Naive Bayes  0.129635              NaN    Median Imputation   
 42  Naive Bayes  0.133063              NaN    Median Imputation   
 43  Naive Bayes       NaN              NaN    Median Imputation   
 44  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 
                                     Model Unique Code  
 0   Naive Bayes_Undersampling_Median Imputation_fo...  
 1   Naive Bayes_Undersampling_Median Imputation_fo...  
 2   Naive Bayes_Undersampling_Median Imputation_fo...  
 3   Naive Bayes_Undersampling_Median Imputation_fo...  
 4   Naive Bayes_Undersampling_Median Imputation_fo...  
 5   Naive Bayes_Undersampling_Median Imputation_fo...  
 6   Naive Bayes_Undersampling_Median Imputation_fo...  
 7   Naive Bayes_Undersampling_Median Imputation_fo...  
 8   Naive Bayes_Undersampling_Median Imputation_fo...  
 9   Naive Bayes_Undersampling_Median Imputation_fo...  
 10  Naive Bayes_Undersampling_Median Imputation_fo...  
 11  Naive Bayes_Undersampling_Median Imputation_fo...  
 12  Naive Bayes_Undersampling_Median Imputation_fo...  
 13  Naive Bayes_Undersampling_Median Imputation_fo...  
 14  Naive Bayes_Undersampling_Median Imputation_fo...  
 15  Naive Bayes_Undersampling_Median Imputation_fo...  
 16  Naive Bayes_Undersampling_Median Imputation_fo...  
 17  Naive Bayes_Undersampling_Median Imputation_fo...  
 18  Naive Bayes_Undersampling_Median Imputation_fo...  
 19  Naive Bayes_Undersampling_Median Imputation_fo...  
 20  Naive Bayes_Undersampling_Median Imputation_fo...  
 21  Naive Bayes_Undersampling_Median Imputation_fo...  
 22  Naive Bayes_Undersampling_Median Imputation_fo...  
 23  Naive Bayes_Undersampling_Median Imputation_fo...  
 24  Naive Bayes_Undersampling_Median Imputation_fo...  
 25  Naive Bayes_Undersampling_Median Imputation_fo...  
 26  Naive Bayes_Undersampling_Median Imputation_fo...  
 27  Naive Bayes_Undersampling_Median Imputation_fo...  
 28  Naive Bayes_Undersampling_Median Imputation_fo...  
 29  Naive Bayes_Undersampling_Median Imputation_fo...  
 30  Naive Bayes_Undersampling_Median Imputation_fo...  
 31  Naive Bayes_Undersampling_Median Imputation_fo...  
 32  Naive Bayes_Undersampling_Median Imputation_fo...  
 33  Naive Bayes_Undersampling_Median Imputation_fo...  
 34  Naive Bayes_Undersampling_Median Imputation_fo...  
 35  Naive Bayes_Undersampling_Median Imputation_fo...  
 36  Naive Bayes_Undersampling_Median Imputation_fo...  
 37  Naive Bayes_Undersampling_Median Imputation_fo...  
 38  Naive Bayes_Undersampling_Median Imputation_fo...  
 39  Naive Bayes_Undersampling_Median Imputation_fo...  
 40  Naive Bayes_Undersampling_Median Imputation_fo...  
 41  Naive Bayes_Undersampling_Median Imputation_fo...  
 42  Naive Bayes_Undersampling_Median Imputation_fo...  
 43  Naive Bayes_Undersampling_Median Imputation_fo...  
 44  Naive Bayes_Undersampling_Median Imputation_fo...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.239663              NaN      Zero Imputation   
 1   Naive Bayes  0.066710              NaN      Zero Imputation   
 2   Naive Bayes  0.860759              NaN      Zero Imputation   
 3   Naive Bayes  0.123824              NaN      Zero Imputation   
 4   Naive Bayes  0.528565              NaN      Zero Imputation   
 5   Naive Bayes  0.061596              NaN      Zero Imputation   
 6   Naive Bayes  0.057130              NaN      Zero Imputation   
 7   Naive Bayes       NaN              NaN      Zero Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 9   Naive Bayes  0.219910              NaN      Zero Imputation   
 10  Naive Bayes  0.044893              NaN      Zero Imputation   
 11  Naive Bayes  0.841463              NaN      Zero Imputation   
 12  Naive Bayes  0.085238              NaN      Zero Imputation   
 13  Naive Bayes  0.520338              NaN      Zero Imputation   
 14  Naive Bayes  0.055266              NaN      Zero Imputation   
 15  Naive Bayes  0.040676              NaN      Zero Imputation   
 16  Naive Bayes       NaN              NaN      Zero Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 18  Naive Bayes  0.224124              NaN      Zero Imputation   
 19  Naive Bayes  0.056003              NaN      Zero Imputation   
 20  Naive Bayes  0.930481              NaN      Zero Imputation   
 21  Naive Bayes  0.105647              NaN      Zero Imputation   
 22  Naive Bayes  0.555513              NaN      Zero Imputation   
 23  Naive Bayes  0.118570              NaN      Zero Imputation   
 24  Naive Bayes  0.111026              NaN      Zero Imputation   
 25  Naive Bayes       NaN              NaN      Zero Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 27  Naive Bayes  0.214436              NaN      Zero Imputation   
 28  Naive Bayes  0.055804              NaN      Zero Imputation   
 29  Naive Bayes  0.892857              NaN      Zero Imputation   
 30  Naive Bayes  0.105042              NaN      Zero Imputation   
 31  Naive Bayes  0.533795              NaN      Zero Imputation   
 32  Naive Bayes  0.073413              NaN      Zero Imputation   
 33  Naive Bayes  0.067591              NaN      Zero Imputation   
 34  Naive Bayes       NaN              NaN      Zero Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 36  Naive Bayes  0.222076              NaN      Zero Imputation   
 37  Naive Bayes  0.064311              NaN      Zero Imputation   
 38  Naive Bayes  0.935185              NaN      Zero Imputation   
 39  Naive Bayes  0.120346              NaN      Zero Imputation   
 40  Naive Bayes  0.562531              NaN      Zero Imputation   
 41  Naive Bayes  0.123174              NaN      Zero Imputation   
 42  Naive Bayes  0.125062              NaN      Zero Imputation   
 43  Naive Bayes       NaN              NaN      Zero Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 1   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 2   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 3   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 4   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 5   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 6   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 7   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 8   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 9   Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 10  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 11  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 12  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 13  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 14  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 15  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 16  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 17  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 18  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 19  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 20  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 21  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 22  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 23  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 24  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 25  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 26  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 27  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 28  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 29  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 30  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 31  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 32  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 33  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 34  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 35  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 36  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 37  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 38  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 39  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 40  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 41  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 42  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 43  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  
 44  Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.229392              NaN      Mean Imputation   
 1   Naive Bayes  0.066430              NaN      Mean Imputation   
 2   Naive Bayes  0.869198              NaN      Mean Imputation   
 3   Naive Bayes  0.123427              NaN      Mean Imputation   
 4   Naive Bayes  0.527628              NaN      Mean Imputation   
 5   Naive Bayes  0.059923              NaN      Mean Imputation   
 6   Naive Bayes  0.055255              NaN      Mean Imputation   
 7   Naive Bayes       NaN              NaN      Mean Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 9   Naive Bayes  0.214643              NaN      Mean Imputation   
 10  Naive Bayes  0.044602              NaN      Mean Imputation   
 11  Naive Bayes  0.841463              NaN      Mean Imputation   
 12  Naive Bayes  0.084715              NaN      Mean Imputation   
 13  Naive Bayes  0.517486              NaN      Mean Imputation   
 14  Naive Bayes  0.050503              NaN      Mean Imputation   
 15  Naive Bayes  0.034972              NaN      Mean Imputation   
 16  Naive Bayes       NaN              NaN      Mean Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 18  Naive Bayes  0.223334              NaN      Mean Imputation   
 19  Naive Bayes  0.055663              NaN      Mean Imputation   
 20  Naive Bayes  0.925134              NaN      Mean Imputation   
 21  Naive Bayes  0.105008              NaN      Mean Imputation   
 22  Naive Bayes  0.554203              NaN      Mean Imputation   
 23  Naive Bayes  0.115077              NaN      Mean Imputation   
 24  Naive Bayes  0.108407              NaN      Mean Imputation   
 25  Naive Bayes       NaN              NaN      Mean Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 27  Naive Bayes  0.209958              NaN      Mean Imputation   
 28  Naive Bayes  0.054938              NaN      Mean Imputation   
 29  Naive Bayes  0.882653              NaN      Mean Imputation   
 30  Naive Bayes  0.103438              NaN      Mean Imputation   
 31  Naive Bayes  0.527458              NaN      Mean Imputation   
 32  Naive Bayes  0.075034              NaN      Mean Imputation   
 33  Naive Bayes  0.054916              NaN      Mean Imputation   
 34  Naive Bayes       NaN              NaN      Mean Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 36  Naive Bayes  0.215227              NaN      Mean Imputation   
 37  Naive Bayes  0.063783              NaN      Mean Imputation   
 38  Naive Bayes  0.935185              NaN      Mean Imputation   
 39  Naive Bayes  0.119421              NaN      Mean Imputation   
 40  Naive Bayes  0.558412              NaN      Mean Imputation   
 41  Naive Bayes  0.115632              NaN      Mean Imputation   
 42  Naive Bayes  0.116824              NaN      Mean Imputation   
 43  Naive Bayes       NaN              NaN      Mean Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 1   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 2   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 3   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 4   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 5   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 6   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 7   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 8   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 9   Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 10  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 11  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 12  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 13  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 14  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 15  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 16  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 17  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 18  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 19  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 20  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 21  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 22  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 23  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 24  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 25  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 26  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 27  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 28  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 29  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 30  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 31  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 32  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 33  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 34  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 35  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 36  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 37  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 38  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 39  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 40  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 41  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 42  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 43  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  
 44  Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.236766              NaN    Median Imputation   
 1   Naive Bayes  0.066471              NaN    Median Imputation   
 2   Naive Bayes  0.860759              NaN    Median Imputation   
 3   Naive Bayes  0.123412              NaN    Median Imputation   
 4   Naive Bayes  0.527737              NaN    Median Imputation   
 5   Naive Bayes  0.059630              NaN    Median Imputation   
 6   Naive Bayes  0.055475              NaN    Median Imputation   
 7   Naive Bayes       NaN              NaN    Median Imputation   
 8   Naive Bayes       NaN     1.000000e-09    Median Imputation   
 9   Naive Bayes  0.216750              NaN    Median Imputation   
 10  Naive Bayes  0.044718              NaN    Median Imputation   
 11  Naive Bayes  0.841463              NaN    Median Imputation   
 12  Naive Bayes  0.084923              NaN    Median Imputation   
 13  Naive Bayes  0.518103              NaN    Median Imputation   
 14  Naive Bayes  0.052980              NaN    Median Imputation   
 15  Naive Bayes  0.036206              NaN    Median Imputation   
 16  Naive Bayes       NaN              NaN    Median Imputation   
 17  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 18  Naive Bayes  0.223598              NaN    Median Imputation   
 19  Naive Bayes  0.055967              NaN    Median Imputation   
 20  Naive Bayes  0.930481              NaN    Median Imputation   
 21  Naive Bayes  0.105583              NaN    Median Imputation   
 22  Naive Bayes  0.557676              NaN    Median Imputation   
 23  Naive Bayes  0.119401              NaN    Median Imputation   
 24  Naive Bayes  0.115351              NaN    Median Imputation   
 25  Naive Bayes       NaN              NaN    Median Imputation   
 26  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 27  Naive Bayes  0.212065              NaN    Median Imputation   
 28  Naive Bayes  0.055644              NaN    Median Imputation   
 29  Naive Bayes  0.892857              NaN    Median Imputation   
 30  Naive Bayes  0.104759              NaN    Median Imputation   
 31  Naive Bayes  0.535581              NaN    Median Imputation   
 32  Naive Bayes  0.074524              NaN    Median Imputation   
 33  Naive Bayes  0.071162              NaN    Median Imputation   
 34  Naive Bayes       NaN              NaN    Median Imputation   
 35  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 36  Naive Bayes  0.222603              NaN    Median Imputation   
 37  Naive Bayes  0.064352              NaN    Median Imputation   
 38  Naive Bayes  0.935185              NaN    Median Imputation   
 39  Naive Bayes  0.120417              NaN    Median Imputation   
 40  Naive Bayes  0.561485              NaN    Median Imputation   
 41  Naive Bayes  0.124291              NaN    Median Imputation   
 42  Naive Bayes  0.122971              NaN    Median Imputation   
 43  Naive Bayes       NaN              NaN    Median Imputation   
 44  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 1   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 2   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 3   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 4   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 5   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 6   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 7   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 8   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 9   Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 10  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 11  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 12  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 13  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 14  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 15  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 16  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 17  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 18  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 19  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 20  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 21  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 22  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 23  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 24  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 25  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 26  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 27  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 28  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 29  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 30  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 31  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 32  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 33  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 34  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 35  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 36  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 37  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 38  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 39  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 40  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 41  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 42  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 43  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  
 44  Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.248881              NaN      Zero Imputation   
 1   Naive Bayes  0.066335              NaN      Zero Imputation   
 2   Naive Bayes  0.843882              NaN      Zero Imputation   
 3   Naive Bayes  0.123001              NaN      Zero Imputation   
 4   Naive Bayes  0.529404              NaN      Zero Imputation   
 5   Naive Bayes  0.062421              NaN      Zero Imputation   
 6   Naive Bayes  0.058809              NaN      Zero Imputation   
 7   Naive Bayes       NaN              NaN      Zero Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 9   Naive Bayes  0.229655              NaN      Zero Imputation   
 10  Naive Bayes  0.045140              NaN      Zero Imputation   
 11  Naive Bayes  0.835366              NaN      Zero Imputation   
 12  Naive Bayes  0.085652              NaN      Zero Imputation   
 13  Naive Bayes  0.522738              NaN      Zero Imputation   
 14  Naive Bayes  0.059628              NaN      Zero Imputation   
 15  Naive Bayes  0.045476              NaN      Zero Imputation   
 16  Naive Bayes       NaN              NaN      Zero Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 18  Naive Bayes  0.232289              NaN      Zero Imputation   
 19  Naive Bayes  0.055990              NaN      Zero Imputation   
 20  Naive Bayes  0.919786              NaN      Zero Imputation   
 21  Naive Bayes  0.105554              NaN      Zero Imputation   
 22  Naive Bayes  0.560588              NaN      Zero Imputation   
 23  Naive Bayes  0.120702              NaN      Zero Imputation   
 24  Naive Bayes  0.121176              NaN      Zero Imputation   
 25  Naive Bayes       NaN              NaN      Zero Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 27  Naive Bayes  0.220495              NaN      Zero Imputation   
 28  Naive Bayes  0.055359              NaN      Zero Imputation   
 29  Naive Bayes  0.877551              NaN      Zero Imputation   
 30  Naive Bayes  0.104148              NaN      Zero Imputation   
 31  Naive Bayes  0.532452              NaN      Zero Imputation   
 32  Naive Bayes  0.071774              NaN      Zero Imputation   
 33  Naive Bayes  0.064904              NaN      Zero Imputation   
 34  Naive Bayes       NaN              NaN      Zero Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 36  Naive Bayes  0.234457              NaN      Zero Imputation   
 37  Naive Bayes  0.064725              NaN      Zero Imputation   
 38  Naive Bayes  0.925926              NaN      Zero Imputation   
 39  Naive Bayes  0.120992              NaN      Zero Imputation   
 40  Naive Bayes  0.556747              NaN      Zero Imputation   
 41  Naive Bayes  0.121177              NaN      Zero Imputation   
 42  Naive Bayes  0.113493              NaN      Zero Imputation   
 43  Naive Bayes       NaN              NaN      Zero Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Zero Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 1   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 2   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 3   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 4   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 5   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 6   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 7   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 8   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 9   Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 10  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 11  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 12  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 13  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 14  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 15  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 16  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 17  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 18  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 19  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 20  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 21  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 22  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 23  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 24  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 25  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 26  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 27  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 28  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 29  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 30  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 31  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 32  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 33  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 34  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 35  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 36  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 37  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 38  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 39  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 40  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 41  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 42  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 43  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  
 44  Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.237029              NaN      Mean Imputation   
 1   Naive Bayes  0.066210              NaN      Mean Imputation   
 2   Naive Bayes  0.856540              NaN      Mean Imputation   
 3   Naive Bayes  0.122919              NaN      Mean Imputation   
 4   Naive Bayes  0.529378              NaN      Mean Imputation   
 5   Naive Bayes  0.063001              NaN      Mean Imputation   
 6   Naive Bayes  0.058756              NaN      Mean Imputation   
 7   Naive Bayes       NaN              NaN      Mean Imputation   
 8   Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 9   Naive Bayes  0.220437              NaN      Mean Imputation   
 10  Naive Bayes  0.044922              NaN      Mean Imputation   
 11  Naive Bayes  0.841463              NaN      Mean Imputation   
 12  Naive Bayes  0.085290              NaN      Mean Imputation   
 13  Naive Bayes  0.519999              NaN      Mean Imputation   
 14  Naive Bayes  0.053045              NaN      Mean Imputation   
 15  Naive Bayes  0.039998              NaN      Mean Imputation   
 16  Naive Bayes       NaN              NaN      Mean Imputation   
 17  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 18  Naive Bayes  0.228865              NaN      Mean Imputation   
 19  Naive Bayes  0.055465              NaN      Mean Imputation   
 20  Naive Bayes  0.914439              NaN      Mean Imputation   
 21  Naive Bayes  0.104587              NaN      Mean Imputation   
 22  Naive Bayes  0.556979              NaN      Mean Imputation   
 23  Naive Bayes  0.116823              NaN      Mean Imputation   
 24  Naive Bayes  0.113957              NaN      Mean Imputation   
 25  Naive Bayes       NaN              NaN      Mean Imputation   
 26  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 27  Naive Bayes  0.215490              NaN      Mean Imputation   
 28  Naive Bayes  0.055307              NaN      Mean Imputation   
 29  Naive Bayes  0.882653              NaN      Mean Imputation   
 30  Naive Bayes  0.104091              NaN      Mean Imputation   
 31  Naive Bayes  0.530223              NaN      Mean Imputation   
 32  Naive Bayes  0.068719              NaN      Mean Imputation   
 33  Naive Bayes  0.060446              NaN      Mean Imputation   
 34  Naive Bayes       NaN              NaN      Mean Imputation   
 35  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 36  Naive Bayes  0.222339              NaN      Mean Imputation   
 37  Naive Bayes  0.062660              NaN      Mean Imputation   
 38  Naive Bayes  0.907407              NaN      Mean Imputation   
 39  Naive Bayes  0.117225              NaN      Mean Imputation   
 40  Naive Bayes  0.549197              NaN      Mean Imputation   
 41  Naive Bayes  0.096276              NaN      Mean Imputation   
 42  Naive Bayes  0.098394              NaN      Mean Imputation   
 43  Naive Bayes       NaN              NaN      Mean Imputation   
 44  Naive Bayes       NaN     1.000000e-09      Mean Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 1   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 2   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 3   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 4   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 5   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 6   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 7   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 8   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 9   Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 10  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 11  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 12  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 13  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 14  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 15  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 16  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 17  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 18  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 19  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 20  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 21  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 22  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 23  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 24  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 25  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 26  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 27  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 28  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 29  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 30  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 31  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 32  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 33  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 34  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 35  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 36  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 37  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 38  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 39  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 40  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 41  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 42  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 43  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  
 44  Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...  ,
       Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0   Naive Bayes  0.240980              NaN    Median Imputation   
 1   Naive Bayes  0.066251              NaN    Median Imputation   
 2   Naive Bayes  0.852321              NaN    Median Imputation   
 3   Naive Bayes  0.122946              NaN    Median Imputation   
 4   Naive Bayes  0.528796              NaN    Median Imputation   
 5   Naive Bayes  0.061590              NaN    Median Imputation   
 6   Naive Bayes  0.057593              NaN    Median Imputation   
 7   Naive Bayes       NaN              NaN    Median Imputation   
 8   Naive Bayes       NaN     1.000000e-09    Median Imputation   
 9   Naive Bayes  0.226231              NaN    Median Imputation   
 10  Naive Bayes  0.044948              NaN    Median Imputation   
 11  Naive Bayes  0.835366              NaN    Median Imputation   
 12  Naive Bayes  0.085305              NaN    Median Imputation   
 13  Naive Bayes  0.519713              NaN    Median Imputation   
 14  Naive Bayes  0.052980              NaN    Median Imputation   
 15  Naive Bayes  0.039425              NaN    Median Imputation   
 16  Naive Bayes       NaN              NaN    Median Imputation   
 17  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 18  Naive Bayes  0.230972              NaN    Median Imputation   
 19  Naive Bayes  0.055610              NaN    Median Imputation   
 20  Naive Bayes  0.914439              NaN    Median Imputation   
 21  Naive Bayes  0.104844              NaN    Median Imputation   
 22  Naive Bayes  0.557534              NaN    Median Imputation   
 23  Naive Bayes  0.114161              NaN    Median Imputation   
 24  Naive Bayes  0.115068              NaN    Median Imputation   
 25  Naive Bayes       NaN              NaN    Median Imputation   
 26  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 27  Naive Bayes  0.224974              NaN    Median Imputation   
 28  Naive Bayes  0.056238              NaN    Median Imputation   
 29  Naive Bayes  0.887755              NaN    Median Imputation   
 30  Naive Bayes  0.105775              NaN    Median Imputation   
 31  Naive Bayes  0.532014              NaN    Median Imputation   
 32  Naive Bayes  0.077477              NaN    Median Imputation   
 33  Naive Bayes  0.064028              NaN    Median Imputation   
 34  Naive Bayes       NaN              NaN    Median Imputation   
 35  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 36  Naive Bayes  0.225501              NaN    Median Imputation   
 37  Naive Bayes  0.063462              NaN    Median Imputation   
 38  Naive Bayes  0.916667              NaN    Median Imputation   
 39  Naive Bayes  0.118705              NaN    Median Imputation   
 40  Naive Bayes  0.553297              NaN    Median Imputation   
 41  Naive Bayes  0.104055              NaN    Median Imputation   
 42  Naive Bayes  0.106594              NaN    Median Imputation   
 43  Naive Bayes       NaN              NaN    Median Imputation   
 44  Naive Bayes       NaN     1.000000e-09    Median Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 1   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 2   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 3   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 4   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 5   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 6   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 7   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 8   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 9   Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 10  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 11  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 12  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 13  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 14  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 15  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 16  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 17  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 18  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 19  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 20  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 21  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 22  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 23  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 24  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 25  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 26  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 27  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 28  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 29  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 30  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 31  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 32  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 33  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 34  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 35  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 36  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 37  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 38  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 39  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 40  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 41  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 42  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 43  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  
 44  Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.873848             NaN      Zero Imputation   
 1   Decision Trees  0.104575             NaN      Zero Imputation   
 2   Decision Trees  0.135021             NaN      Zero Imputation   
 3   Decision Trees  0.117864             NaN      Zero Imputation   
 4   Decision Trees  0.528177             NaN      Zero Imputation   
 5   Decision Trees  0.058055             NaN      Zero Imputation   
 6   Decision Trees  0.056354             NaN      Zero Imputation   
 7   Decision Trees       NaN             0.0      Zero Imputation   
 8   Decision Trees       NaN            None      Zero Imputation   
 9   Decision Trees       NaN            gini      Zero Imputation   
 10  Decision Trees       NaN            None      Zero Imputation   
 11  Decision Trees       NaN            None      Zero Imputation   
 12  Decision Trees       NaN            None      Zero Imputation   
 13  Decision Trees       NaN             0.0      Zero Imputation   
 14  Decision Trees       NaN               1      Zero Imputation   
 15  Decision Trees       NaN               2      Zero Imputation   
 16  Decision Trees       NaN             0.0      Zero Imputation   
 17  Decision Trees       NaN            None      Zero Imputation   
 18  Decision Trees       NaN            best      Zero Imputation   
 19  Decision Trees  0.887016             NaN      Zero Imputation   
 20  Decision Trees  0.084639             NaN      Zero Imputation   
 21  Decision Trees  0.164634             NaN      Zero Imputation   
 22  Decision Trees  0.111801             NaN      Zero Imputation   
 23  Decision Trees  0.546918             NaN      Zero Imputation   
 24  Decision Trees  0.100350             NaN      Zero Imputation   
 25  Decision Trees  0.093837             NaN      Zero Imputation   
 26  Decision Trees       NaN             0.0      Zero Imputation   
 27  Decision Trees       NaN            None      Zero Imputation   
 28  Decision Trees       NaN            gini      Zero Imputation   
 29  Decision Trees       NaN            None      Zero Imputation   
 30  Decision Trees       NaN            None      Zero Imputation   
 31  Decision Trees       NaN            None      Zero Imputation   
 32  Decision Trees       NaN             0.0      Zero Imputation   
 33  Decision Trees       NaN               1      Zero Imputation   
 34  Decision Trees       NaN               2      Zero Imputation   
 35  Decision Trees       NaN             0.0      Zero Imputation   
 36  Decision Trees       NaN            None      Zero Imputation   
 37  Decision Trees       NaN            best      Zero Imputation   
 38  Decision Trees  0.875165             NaN      Zero Imputation   
 39  Decision Trees  0.106849             NaN      Zero Imputation   
 40  Decision Trees  0.208556             NaN      Zero Imputation   
 41  Decision Trees  0.141304             NaN      Zero Imputation   
 42  Decision Trees  0.555618             NaN      Zero Imputation   
 43  Decision Trees  0.119359             NaN      Zero Imputation   
 44  Decision Trees  0.111236             NaN      Zero Imputation   
 45  Decision Trees       NaN             0.0      Zero Imputation   
 46  Decision Trees       NaN            None      Zero Imputation   
 47  Decision Trees       NaN            gini      Zero Imputation   
 48  Decision Trees       NaN            None      Zero Imputation   
 49  Decision Trees       NaN            None      Zero Imputation   
 50  Decision Trees       NaN            None      Zero Imputation   
 51  Decision Trees       NaN             0.0      Zero Imputation   
 52  Decision Trees       NaN               1      Zero Imputation   
 53  Decision Trees       NaN               2      Zero Imputation   
 54  Decision Trees       NaN             0.0      Zero Imputation   
 55  Decision Trees       NaN            None      Zero Imputation   
 56  Decision Trees       NaN            best      Zero Imputation   
 57  Decision Trees  0.874868             NaN      Zero Imputation   
 58  Decision Trees  0.088496             NaN      Zero Imputation   
 59  Decision Trees  0.153061             NaN      Zero Imputation   
 60  Decision Trees  0.112150             NaN      Zero Imputation   
 61  Decision Trees  0.531580             NaN      Zero Imputation   
 62  Decision Trees  0.070283             NaN      Zero Imputation   
 63  Decision Trees  0.063160             NaN      Zero Imputation   
 64  Decision Trees       NaN             0.0      Zero Imputation   
 65  Decision Trees       NaN            None      Zero Imputation   
 66  Decision Trees       NaN            gini      Zero Imputation   
 67  Decision Trees       NaN            None      Zero Imputation   
 68  Decision Trees       NaN            None      Zero Imputation   
 69  Decision Trees       NaN            None      Zero Imputation   
 70  Decision Trees       NaN             0.0      Zero Imputation   
 71  Decision Trees       NaN               1      Zero Imputation   
 72  Decision Trees       NaN               2      Zero Imputation   
 73  Decision Trees       NaN             0.0      Zero Imputation   
 74  Decision Trees       NaN            None      Zero Imputation   
 75  Decision Trees       NaN            best      Zero Imputation   
 76  Decision Trees  0.877503             NaN      Zero Imputation   
 77  Decision Trees  0.121581             NaN      Zero Imputation   
 78  Decision Trees  0.185185             NaN      Zero Imputation   
 79  Decision Trees  0.146789             NaN      Zero Imputation   
 80  Decision Trees  0.550908             NaN      Zero Imputation   
 81  Decision Trees  0.104459             NaN      Zero Imputation   
 82  Decision Trees  0.101816             NaN      Zero Imputation   
 83  Decision Trees       NaN             0.0      Zero Imputation   
 84  Decision Trees       NaN            None      Zero Imputation   
 85  Decision Trees       NaN            gini      Zero Imputation   
 86  Decision Trees       NaN            None      Zero Imputation   
 87  Decision Trees       NaN            None      Zero Imputation   
 88  Decision Trees       NaN            None      Zero Imputation   
 89  Decision Trees       NaN             0.0      Zero Imputation   
 90  Decision Trees       NaN               1      Zero Imputation   
 91  Decision Trees       NaN               2      Zero Imputation   
 92  Decision Trees       NaN             0.0      Zero Imputation   
 93  Decision Trees       NaN            None      Zero Imputation   
 94  Decision Trees       NaN            best      Zero Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 75              Oversampling   
 76              Oversampling   
 77              Oversampling   
 78              Oversampling   
 79              Oversampling   
 80              Oversampling   
 81              Oversampling   
 82              Oversampling   
 83              Oversampling   
 84              Oversampling   
 85              Oversampling   
 86              Oversampling   
 87              Oversampling   
 88              Oversampling   
 89              Oversampling   
 90              Oversampling   
 91              Oversampling   
 92              Oversampling   
 93              Oversampling   
 94              Oversampling   
 
                                     Model Unique Code  
 0   Decision Trees_Oversampling_Zero Imputation_fo...  
 1   Decision Trees_Oversampling_Zero Imputation_fo...  
 2   Decision Trees_Oversampling_Zero Imputation_fo...  
 3   Decision Trees_Oversampling_Zero Imputation_fo...  
 4   Decision Trees_Oversampling_Zero Imputation_fo...  
 5   Decision Trees_Oversampling_Zero Imputation_fo...  
 6   Decision Trees_Oversampling_Zero Imputation_fo...  
 7   Decision Trees_Oversampling_Zero Imputation_fo...  
 8   Decision Trees_Oversampling_Zero Imputation_fo...  
 9   Decision Trees_Oversampling_Zero Imputation_fo...  
 10  Decision Trees_Oversampling_Zero Imputation_fo...  
 11  Decision Trees_Oversampling_Zero Imputation_fo...  
 12  Decision Trees_Oversampling_Zero Imputation_fo...  
 13  Decision Trees_Oversampling_Zero Imputation_fo...  
 14  Decision Trees_Oversampling_Zero Imputation_fo...  
 15  Decision Trees_Oversampling_Zero Imputation_fo...  
 16  Decision Trees_Oversampling_Zero Imputation_fo...  
 17  Decision Trees_Oversampling_Zero Imputation_fo...  
 18  Decision Trees_Oversampling_Zero Imputation_fo...  
 19  Decision Trees_Oversampling_Zero Imputation_fo...  
 20  Decision Trees_Oversampling_Zero Imputation_fo...  
 21  Decision Trees_Oversampling_Zero Imputation_fo...  
 22  Decision Trees_Oversampling_Zero Imputation_fo...  
 23  Decision Trees_Oversampling_Zero Imputation_fo...  
 24  Decision Trees_Oversampling_Zero Imputation_fo...  
 25  Decision Trees_Oversampling_Zero Imputation_fo...  
 26  Decision Trees_Oversampling_Zero Imputation_fo...  
 27  Decision Trees_Oversampling_Zero Imputation_fo...  
 28  Decision Trees_Oversampling_Zero Imputation_fo...  
 29  Decision Trees_Oversampling_Zero Imputation_fo...  
 30  Decision Trees_Oversampling_Zero Imputation_fo...  
 31  Decision Trees_Oversampling_Zero Imputation_fo...  
 32  Decision Trees_Oversampling_Zero Imputation_fo...  
 33  Decision Trees_Oversampling_Zero Imputation_fo...  
 34  Decision Trees_Oversampling_Zero Imputation_fo...  
 35  Decision Trees_Oversampling_Zero Imputation_fo...  
 36  Decision Trees_Oversampling_Zero Imputation_fo...  
 37  Decision Trees_Oversampling_Zero Imputation_fo...  
 38  Decision Trees_Oversampling_Zero Imputation_fo...  
 39  Decision Trees_Oversampling_Zero Imputation_fo...  
 40  Decision Trees_Oversampling_Zero Imputation_fo...  
 41  Decision Trees_Oversampling_Zero Imputation_fo...  
 42  Decision Trees_Oversampling_Zero Imputation_fo...  
 43  Decision Trees_Oversampling_Zero Imputation_fo...  
 44  Decision Trees_Oversampling_Zero Imputation_fo...  
 45  Decision Trees_Oversampling_Zero Imputation_fo...  
 46  Decision Trees_Oversampling_Zero Imputation_fo...  
 47  Decision Trees_Oversampling_Zero Imputation_fo...  
 48  Decision Trees_Oversampling_Zero Imputation_fo...  
 49  Decision Trees_Oversampling_Zero Imputation_fo...  
 50  Decision Trees_Oversampling_Zero Imputation_fo...  
 51  Decision Trees_Oversampling_Zero Imputation_fo...  
 52  Decision Trees_Oversampling_Zero Imputation_fo...  
 53  Decision Trees_Oversampling_Zero Imputation_fo...  
 54  Decision Trees_Oversampling_Zero Imputation_fo...  
 55  Decision Trees_Oversampling_Zero Imputation_fo...  
 56  Decision Trees_Oversampling_Zero Imputation_fo...  
 57  Decision Trees_Oversampling_Zero Imputation_fo...  
 58  Decision Trees_Oversampling_Zero Imputation_fo...  
 59  Decision Trees_Oversampling_Zero Imputation_fo...  
 60  Decision Trees_Oversampling_Zero Imputation_fo...  
 61  Decision Trees_Oversampling_Zero Imputation_fo...  
 62  Decision Trees_Oversampling_Zero Imputation_fo...  
 63  Decision Trees_Oversampling_Zero Imputation_fo...  
 64  Decision Trees_Oversampling_Zero Imputation_fo...  
 65  Decision Trees_Oversampling_Zero Imputation_fo...  
 66  Decision Trees_Oversampling_Zero Imputation_fo...  
 67  Decision Trees_Oversampling_Zero Imputation_fo...  
 68  Decision Trees_Oversampling_Zero Imputation_fo...  
 69  Decision Trees_Oversampling_Zero Imputation_fo...  
 70  Decision Trees_Oversampling_Zero Imputation_fo...  
 71  Decision Trees_Oversampling_Zero Imputation_fo...  
 72  Decision Trees_Oversampling_Zero Imputation_fo...  
 73  Decision Trees_Oversampling_Zero Imputation_fo...  
 74  Decision Trees_Oversampling_Zero Imputation_fo...  
 75  Decision Trees_Oversampling_Zero Imputation_fo...  
 76  Decision Trees_Oversampling_Zero Imputation_fo...  
 77  Decision Trees_Oversampling_Zero Imputation_fo...  
 78  Decision Trees_Oversampling_Zero Imputation_fo...  
 79  Decision Trees_Oversampling_Zero Imputation_fo...  
 80  Decision Trees_Oversampling_Zero Imputation_fo...  
 81  Decision Trees_Oversampling_Zero Imputation_fo...  
 82  Decision Trees_Oversampling_Zero Imputation_fo...  
 83  Decision Trees_Oversampling_Zero Imputation_fo...  
 84  Decision Trees_Oversampling_Zero Imputation_fo...  
 85  Decision Trees_Oversampling_Zero Imputation_fo...  
 86  Decision Trees_Oversampling_Zero Imputation_fo...  
 87  Decision Trees_Oversampling_Zero Imputation_fo...  
 88  Decision Trees_Oversampling_Zero Imputation_fo...  
 89  Decision Trees_Oversampling_Zero Imputation_fo...  
 90  Decision Trees_Oversampling_Zero Imputation_fo...  
 91  Decision Trees_Oversampling_Zero Imputation_fo...  
 92  Decision Trees_Oversampling_Zero Imputation_fo...  
 93  Decision Trees_Oversampling_Zero Imputation_fo...  
 94  Decision Trees_Oversampling_Zero Imputation_fo...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.874638             NaN      Mean Imputation   
 1   Decision Trees  0.118211             NaN      Mean Imputation   
 2   Decision Trees  0.156118             NaN      Mean Imputation   
 3   Decision Trees  0.134545             NaN      Mean Imputation   
 4   Decision Trees  0.539191             NaN      Mean Imputation   
 5   Decision Trees  0.078590             NaN      Mean Imputation   
 6   Decision Trees  0.078383             NaN      Mean Imputation   
 7   Decision Trees       NaN             0.0      Mean Imputation   
 8   Decision Trees       NaN            None      Mean Imputation   
 9   Decision Trees       NaN            gini      Mean Imputation   
 10  Decision Trees       NaN            None      Mean Imputation   
 11  Decision Trees       NaN            None      Mean Imputation   
 12  Decision Trees       NaN            None      Mean Imputation   
 13  Decision Trees       NaN             0.0      Mean Imputation   
 14  Decision Trees       NaN               1      Mean Imputation   
 15  Decision Trees       NaN               2      Mean Imputation   
 16  Decision Trees       NaN             0.0      Mean Imputation   
 17  Decision Trees       NaN            None      Mean Imputation   
 18  Decision Trees       NaN            best      Mean Imputation   
 19  Decision Trees  0.882275             NaN      Mean Imputation   
 20  Decision Trees  0.094556             NaN      Mean Imputation   
 21  Decision Trees  0.201220             NaN      Mean Imputation   
 22  Decision Trees  0.128655             NaN      Mean Imputation   
 23  Decision Trees  0.562656             NaN      Mean Imputation   
 24  Decision Trees  0.130330             NaN      Mean Imputation   
 25  Decision Trees  0.125311             NaN      Mean Imputation   
 26  Decision Trees       NaN             0.0      Mean Imputation   
 27  Decision Trees       NaN            None      Mean Imputation   
 28  Decision Trees       NaN            gini      Mean Imputation   
 29  Decision Trees       NaN            None      Mean Imputation   
 30  Decision Trees       NaN            None      Mean Imputation   
 31  Decision Trees       NaN            None      Mean Imputation   
 32  Decision Trees       NaN             0.0      Mean Imputation   
 33  Decision Trees       NaN               1      Mean Imputation   
 34  Decision Trees       NaN               2      Mean Imputation   
 35  Decision Trees       NaN             0.0      Mean Imputation   
 36  Decision Trees       NaN            None      Mean Imputation   
 37  Decision Trees       NaN            best      Mean Imputation   
 38  Decision Trees  0.873848             NaN      Mean Imputation   
 39  Decision Trees  0.109626             NaN      Mean Imputation   
 40  Decision Trees  0.219251             NaN      Mean Imputation   
 41  Decision Trees  0.146168             NaN      Mean Imputation   
 42  Decision Trees  0.560513             NaN      Mean Imputation   
 43  Decision Trees  0.127839             NaN      Mean Imputation   
 44  Decision Trees  0.121026             NaN      Mean Imputation   
 45  Decision Trees       NaN             0.0      Mean Imputation   
 46  Decision Trees       NaN            None      Mean Imputation   
 47  Decision Trees       NaN            gini      Mean Imputation   
 48  Decision Trees       NaN            None      Mean Imputation   
 49  Decision Trees       NaN            None      Mean Imputation   
 50  Decision Trees       NaN            None      Mean Imputation   
 51  Decision Trees       NaN             0.0      Mean Imputation   
 52  Decision Trees       NaN               1      Mean Imputation   
 53  Decision Trees       NaN               2      Mean Imputation   
 54  Decision Trees       NaN             0.0      Mean Imputation   
 55  Decision Trees       NaN            None      Mean Imputation   
 56  Decision Trees       NaN            best      Mean Imputation   
 57  Decision Trees  0.878820             NaN      Mean Imputation   
 58  Decision Trees  0.107143             NaN      Mean Imputation   
 59  Decision Trees  0.183673             NaN      Mean Imputation   
 60  Decision Trees  0.135338             NaN      Mean Imputation   
 61  Decision Trees  0.548421             NaN      Mean Imputation   
 62  Decision Trees  0.102562             NaN      Mean Imputation   
 63  Decision Trees  0.096842             NaN      Mean Imputation   
 64  Decision Trees       NaN             0.0      Mean Imputation   
 65  Decision Trees       NaN            None      Mean Imputation   
 66  Decision Trees       NaN            gini      Mean Imputation   
 67  Decision Trees       NaN            None      Mean Imputation   
 68  Decision Trees       NaN            None      Mean Imputation   
 69  Decision Trees       NaN            None      Mean Imputation   
 70  Decision Trees       NaN             0.0      Mean Imputation   
 71  Decision Trees       NaN               1      Mean Imputation   
 72  Decision Trees       NaN               2      Mean Imputation   
 73  Decision Trees       NaN             0.0      Mean Imputation   
 74  Decision Trees       NaN            None      Mean Imputation   
 75  Decision Trees       NaN            best      Mean Imputation   
 76  Decision Trees  0.873024             NaN      Mean Imputation   
 77  Decision Trees  0.101796             NaN      Mean Imputation   
 78  Decision Trees  0.157407             NaN      Mean Imputation   
 79  Decision Trees  0.123636             NaN      Mean Imputation   
 80  Decision Trees  0.534819             NaN      Mean Imputation   
 81  Decision Trees  0.073609             NaN      Mean Imputation   
 82  Decision Trees  0.069638             NaN      Mean Imputation   
 83  Decision Trees       NaN             0.0      Mean Imputation   
 84  Decision Trees       NaN            None      Mean Imputation   
 85  Decision Trees       NaN            gini      Mean Imputation   
 86  Decision Trees       NaN            None      Mean Imputation   
 87  Decision Trees       NaN            None      Mean Imputation   
 88  Decision Trees       NaN            None      Mean Imputation   
 89  Decision Trees       NaN             0.0      Mean Imputation   
 90  Decision Trees       NaN               1      Mean Imputation   
 91  Decision Trees       NaN               2      Mean Imputation   
 92  Decision Trees       NaN             0.0      Mean Imputation   
 93  Decision Trees       NaN            None      Mean Imputation   
 94  Decision Trees       NaN            best      Mean Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 75              Oversampling   
 76              Oversampling   
 77              Oversampling   
 78              Oversampling   
 79              Oversampling   
 80              Oversampling   
 81              Oversampling   
 82              Oversampling   
 83              Oversampling   
 84              Oversampling   
 85              Oversampling   
 86              Oversampling   
 87              Oversampling   
 88              Oversampling   
 89              Oversampling   
 90              Oversampling   
 91              Oversampling   
 92              Oversampling   
 93              Oversampling   
 94              Oversampling   
 
                                     Model Unique Code  
 0   Decision Trees_Oversampling_Mean Imputation_fo...  
 1   Decision Trees_Oversampling_Mean Imputation_fo...  
 2   Decision Trees_Oversampling_Mean Imputation_fo...  
 3   Decision Trees_Oversampling_Mean Imputation_fo...  
 4   Decision Trees_Oversampling_Mean Imputation_fo...  
 5   Decision Trees_Oversampling_Mean Imputation_fo...  
 6   Decision Trees_Oversampling_Mean Imputation_fo...  
 7   Decision Trees_Oversampling_Mean Imputation_fo...  
 8   Decision Trees_Oversampling_Mean Imputation_fo...  
 9   Decision Trees_Oversampling_Mean Imputation_fo...  
 10  Decision Trees_Oversampling_Mean Imputation_fo...  
 11  Decision Trees_Oversampling_Mean Imputation_fo...  
 12  Decision Trees_Oversampling_Mean Imputation_fo...  
 13  Decision Trees_Oversampling_Mean Imputation_fo...  
 14  Decision Trees_Oversampling_Mean Imputation_fo...  
 15  Decision Trees_Oversampling_Mean Imputation_fo...  
 16  Decision Trees_Oversampling_Mean Imputation_fo...  
 17  Decision Trees_Oversampling_Mean Imputation_fo...  
 18  Decision Trees_Oversampling_Mean Imputation_fo...  
 19  Decision Trees_Oversampling_Mean Imputation_fo...  
 20  Decision Trees_Oversampling_Mean Imputation_fo...  
 21  Decision Trees_Oversampling_Mean Imputation_fo...  
 22  Decision Trees_Oversampling_Mean Imputation_fo...  
 23  Decision Trees_Oversampling_Mean Imputation_fo...  
 24  Decision Trees_Oversampling_Mean Imputation_fo...  
 25  Decision Trees_Oversampling_Mean Imputation_fo...  
 26  Decision Trees_Oversampling_Mean Imputation_fo...  
 27  Decision Trees_Oversampling_Mean Imputation_fo...  
 28  Decision Trees_Oversampling_Mean Imputation_fo...  
 29  Decision Trees_Oversampling_Mean Imputation_fo...  
 30  Decision Trees_Oversampling_Mean Imputation_fo...  
 31  Decision Trees_Oversampling_Mean Imputation_fo...  
 32  Decision Trees_Oversampling_Mean Imputation_fo...  
 33  Decision Trees_Oversampling_Mean Imputation_fo...  
 34  Decision Trees_Oversampling_Mean Imputation_fo...  
 35  Decision Trees_Oversampling_Mean Imputation_fo...  
 36  Decision Trees_Oversampling_Mean Imputation_fo...  
 37  Decision Trees_Oversampling_Mean Imputation_fo...  
 38  Decision Trees_Oversampling_Mean Imputation_fo...  
 39  Decision Trees_Oversampling_Mean Imputation_fo...  
 40  Decision Trees_Oversampling_Mean Imputation_fo...  
 41  Decision Trees_Oversampling_Mean Imputation_fo...  
 42  Decision Trees_Oversampling_Mean Imputation_fo...  
 43  Decision Trees_Oversampling_Mean Imputation_fo...  
 44  Decision Trees_Oversampling_Mean Imputation_fo...  
 45  Decision Trees_Oversampling_Mean Imputation_fo...  
 46  Decision Trees_Oversampling_Mean Imputation_fo...  
 47  Decision Trees_Oversampling_Mean Imputation_fo...  
 48  Decision Trees_Oversampling_Mean Imputation_fo...  
 49  Decision Trees_Oversampling_Mean Imputation_fo...  
 50  Decision Trees_Oversampling_Mean Imputation_fo...  
 51  Decision Trees_Oversampling_Mean Imputation_fo...  
 52  Decision Trees_Oversampling_Mean Imputation_fo...  
 53  Decision Trees_Oversampling_Mean Imputation_fo...  
 54  Decision Trees_Oversampling_Mean Imputation_fo...  
 55  Decision Trees_Oversampling_Mean Imputation_fo...  
 56  Decision Trees_Oversampling_Mean Imputation_fo...  
 57  Decision Trees_Oversampling_Mean Imputation_fo...  
 58  Decision Trees_Oversampling_Mean Imputation_fo...  
 59  Decision Trees_Oversampling_Mean Imputation_fo...  
 60  Decision Trees_Oversampling_Mean Imputation_fo...  
 61  Decision Trees_Oversampling_Mean Imputation_fo...  
 62  Decision Trees_Oversampling_Mean Imputation_fo...  
 63  Decision Trees_Oversampling_Mean Imputation_fo...  
 64  Decision Trees_Oversampling_Mean Imputation_fo...  
 65  Decision Trees_Oversampling_Mean Imputation_fo...  
 66  Decision Trees_Oversampling_Mean Imputation_fo...  
 67  Decision Trees_Oversampling_Mean Imputation_fo...  
 68  Decision Trees_Oversampling_Mean Imputation_fo...  
 69  Decision Trees_Oversampling_Mean Imputation_fo...  
 70  Decision Trees_Oversampling_Mean Imputation_fo...  
 71  Decision Trees_Oversampling_Mean Imputation_fo...  
 72  Decision Trees_Oversampling_Mean Imputation_fo...  
 73  Decision Trees_Oversampling_Mean Imputation_fo...  
 74  Decision Trees_Oversampling_Mean Imputation_fo...  
 75  Decision Trees_Oversampling_Mean Imputation_fo...  
 76  Decision Trees_Oversampling_Mean Imputation_fo...  
 77  Decision Trees_Oversampling_Mean Imputation_fo...  
 78  Decision Trees_Oversampling_Mean Imputation_fo...  
 79  Decision Trees_Oversampling_Mean Imputation_fo...  
 80  Decision Trees_Oversampling_Mean Imputation_fo...  
 81  Decision Trees_Oversampling_Mean Imputation_fo...  
 82  Decision Trees_Oversampling_Mean Imputation_fo...  
 83  Decision Trees_Oversampling_Mean Imputation_fo...  
 84  Decision Trees_Oversampling_Mean Imputation_fo...  
 85  Decision Trees_Oversampling_Mean Imputation_fo...  
 86  Decision Trees_Oversampling_Mean Imputation_fo...  
 87  Decision Trees_Oversampling_Mean Imputation_fo...  
 88  Decision Trees_Oversampling_Mean Imputation_fo...  
 89  Decision Trees_Oversampling_Mean Imputation_fo...  
 90  Decision Trees_Oversampling_Mean Imputation_fo...  
 91  Decision Trees_Oversampling_Mean Imputation_fo...  
 92  Decision Trees_Oversampling_Mean Imputation_fo...  
 93  Decision Trees_Oversampling_Mean Imputation_fo...  
 94  Decision Trees_Oversampling_Mean Imputation_fo...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.867264             NaN    Median Imputation   
 1   Decision Trees  0.101493             NaN    Median Imputation   
 2   Decision Trees  0.143460             NaN    Median Imputation   
 3   Decision Trees  0.118881             NaN    Median Imputation   
 4   Decision Trees  0.528470             NaN    Median Imputation   
 5   Decision Trees  0.059471             NaN    Median Imputation   
 6   Decision Trees  0.056941             NaN    Median Imputation   
 7   Decision Trees       NaN             0.0    Median Imputation   
 8   Decision Trees       NaN            None    Median Imputation   
 9   Decision Trees       NaN            gini    Median Imputation   
 10  Decision Trees       NaN            None    Median Imputation   
 11  Decision Trees       NaN            None    Median Imputation   
 12  Decision Trees       NaN            None    Median Imputation   
 13  Decision Trees       NaN             0.0    Median Imputation   
 14  Decision Trees       NaN               1    Median Imputation   
 15  Decision Trees       NaN               2    Median Imputation   
 16  Decision Trees       NaN             0.0    Median Imputation   
 17  Decision Trees       NaN            None    Median Imputation   
 18  Decision Trees       NaN            best    Median Imputation   
 19  Decision Trees  0.882539             NaN    Median Imputation   
 20  Decision Trees  0.103933             NaN    Median Imputation   
 21  Decision Trees  0.225610             NaN    Median Imputation   
 22  Decision Trees  0.142308             NaN    Median Imputation   
 23  Decision Trees  0.566118             NaN    Median Imputation   
 24  Decision Trees  0.137804             NaN    Median Imputation   
 25  Decision Trees  0.132236             NaN    Median Imputation   
 26  Decision Trees       NaN             0.0    Median Imputation   
 27  Decision Trees       NaN            None    Median Imputation   
 28  Decision Trees       NaN            gini    Median Imputation   
 29  Decision Trees       NaN            None    Median Imputation   
 30  Decision Trees       NaN            None    Median Imputation   
 31  Decision Trees       NaN            None    Median Imputation   
 32  Decision Trees       NaN             0.0    Median Imputation   
 33  Decision Trees       NaN               1    Median Imputation   
 34  Decision Trees       NaN               2    Median Imputation   
 35  Decision Trees       NaN             0.0    Median Imputation   
 36  Decision Trees       NaN            None    Median Imputation   
 37  Decision Trees       NaN            best    Median Imputation   
 38  Decision Trees  0.881485             NaN    Median Imputation   
 39  Decision Trees  0.121037             NaN    Median Imputation   
 40  Decision Trees  0.224599             NaN    Median Imputation   
 41  Decision Trees  0.157303             NaN    Median Imputation   
 42  Decision Trees  0.567455             NaN    Median Imputation   
 43  Decision Trees  0.141219             NaN    Median Imputation   
 44  Decision Trees  0.134910             NaN    Median Imputation   
 45  Decision Trees       NaN             0.0    Median Imputation   
 46  Decision Trees       NaN            None    Median Imputation   
 47  Decision Trees       NaN            gini    Median Imputation   
 48  Decision Trees       NaN            None    Median Imputation   
 49  Decision Trees       NaN            None    Median Imputation   
 50  Decision Trees       NaN            None    Median Imputation   
 51  Decision Trees       NaN             0.0    Median Imputation   
 52  Decision Trees       NaN               1    Median Imputation   
 53  Decision Trees       NaN               2    Median Imputation   
 54  Decision Trees       NaN             0.0    Median Imputation   
 55  Decision Trees       NaN            None    Median Imputation   
 56  Decision Trees       NaN            best    Median Imputation   
 57  Decision Trees  0.871970             NaN    Median Imputation   
 58  Decision Trees  0.080925             NaN    Median Imputation   
 59  Decision Trees  0.142857             NaN    Median Imputation   
 60  Decision Trees  0.103321             NaN    Median Imputation   
 61  Decision Trees  0.524960             NaN    Median Imputation   
 62  Decision Trees  0.055079             NaN    Median Imputation   
 63  Decision Trees  0.049919             NaN    Median Imputation   
 64  Decision Trees       NaN             0.0    Median Imputation   
 65  Decision Trees       NaN            None    Median Imputation   
 66  Decision Trees       NaN            gini    Median Imputation   
 67  Decision Trees       NaN            None    Median Imputation   
 68  Decision Trees       NaN            None    Median Imputation   
 69  Decision Trees       NaN            None    Median Imputation   
 70  Decision Trees       NaN             0.0    Median Imputation   
 71  Decision Trees       NaN               1    Median Imputation   
 72  Decision Trees       NaN               2    Median Imputation   
 73  Decision Trees       NaN             0.0    Median Imputation   
 74  Decision Trees       NaN            None    Median Imputation   
 75  Decision Trees       NaN            best    Median Imputation   
 76  Decision Trees  0.874341             NaN    Median Imputation   
 77  Decision Trees  0.112760             NaN    Median Imputation   
 78  Decision Trees  0.175926             NaN    Median Imputation   
 79  Decision Trees  0.137432             NaN    Median Imputation   
 80  Decision Trees  0.544465             NaN    Median Imputation   
 81  Decision Trees  0.092406             NaN    Median Imputation   
 82  Decision Trees  0.088930             NaN    Median Imputation   
 83  Decision Trees       NaN             0.0    Median Imputation   
 84  Decision Trees       NaN            None    Median Imputation   
 85  Decision Trees       NaN            gini    Median Imputation   
 86  Decision Trees       NaN            None    Median Imputation   
 87  Decision Trees       NaN            None    Median Imputation   
 88  Decision Trees       NaN            None    Median Imputation   
 89  Decision Trees       NaN             0.0    Median Imputation   
 90  Decision Trees       NaN               1    Median Imputation   
 91  Decision Trees       NaN               2    Median Imputation   
 92  Decision Trees       NaN             0.0    Median Imputation   
 93  Decision Trees       NaN            None    Median Imputation   
 94  Decision Trees       NaN            best    Median Imputation   
 
    Imbalance Class Technique  \
 0               Oversampling   
 1               Oversampling   
 2               Oversampling   
 3               Oversampling   
 4               Oversampling   
 5               Oversampling   
 6               Oversampling   
 7               Oversampling   
 8               Oversampling   
 9               Oversampling   
 10              Oversampling   
 11              Oversampling   
 12              Oversampling   
 13              Oversampling   
 14              Oversampling   
 15              Oversampling   
 16              Oversampling   
 17              Oversampling   
 18              Oversampling   
 19              Oversampling   
 20              Oversampling   
 21              Oversampling   
 22              Oversampling   
 23              Oversampling   
 24              Oversampling   
 25              Oversampling   
 26              Oversampling   
 27              Oversampling   
 28              Oversampling   
 29              Oversampling   
 30              Oversampling   
 31              Oversampling   
 32              Oversampling   
 33              Oversampling   
 34              Oversampling   
 35              Oversampling   
 36              Oversampling   
 37              Oversampling   
 38              Oversampling   
 39              Oversampling   
 40              Oversampling   
 41              Oversampling   
 42              Oversampling   
 43              Oversampling   
 44              Oversampling   
 45              Oversampling   
 46              Oversampling   
 47              Oversampling   
 48              Oversampling   
 49              Oversampling   
 50              Oversampling   
 51              Oversampling   
 52              Oversampling   
 53              Oversampling   
 54              Oversampling   
 55              Oversampling   
 56              Oversampling   
 57              Oversampling   
 58              Oversampling   
 59              Oversampling   
 60              Oversampling   
 61              Oversampling   
 62              Oversampling   
 63              Oversampling   
 64              Oversampling   
 65              Oversampling   
 66              Oversampling   
 67              Oversampling   
 68              Oversampling   
 69              Oversampling   
 70              Oversampling   
 71              Oversampling   
 72              Oversampling   
 73              Oversampling   
 74              Oversampling   
 75              Oversampling   
 76              Oversampling   
 77              Oversampling   
 78              Oversampling   
 79              Oversampling   
 80              Oversampling   
 81              Oversampling   
 82              Oversampling   
 83              Oversampling   
 84              Oversampling   
 85              Oversampling   
 86              Oversampling   
 87              Oversampling   
 88              Oversampling   
 89              Oversampling   
 90              Oversampling   
 91              Oversampling   
 92              Oversampling   
 93              Oversampling   
 94              Oversampling   
 
                                     Model Unique Code  
 0   Decision Trees_Oversampling_Median Imputation_...  
 1   Decision Trees_Oversampling_Median Imputation_...  
 2   Decision Trees_Oversampling_Median Imputation_...  
 3   Decision Trees_Oversampling_Median Imputation_...  
 4   Decision Trees_Oversampling_Median Imputation_...  
 5   Decision Trees_Oversampling_Median Imputation_...  
 6   Decision Trees_Oversampling_Median Imputation_...  
 7   Decision Trees_Oversampling_Median Imputation_...  
 8   Decision Trees_Oversampling_Median Imputation_...  
 9   Decision Trees_Oversampling_Median Imputation_...  
 10  Decision Trees_Oversampling_Median Imputation_...  
 11  Decision Trees_Oversampling_Median Imputation_...  
 12  Decision Trees_Oversampling_Median Imputation_...  
 13  Decision Trees_Oversampling_Median Imputation_...  
 14  Decision Trees_Oversampling_Median Imputation_...  
 15  Decision Trees_Oversampling_Median Imputation_...  
 16  Decision Trees_Oversampling_Median Imputation_...  
 17  Decision Trees_Oversampling_Median Imputation_...  
 18  Decision Trees_Oversampling_Median Imputation_...  
 19  Decision Trees_Oversampling_Median Imputation_...  
 20  Decision Trees_Oversampling_Median Imputation_...  
 21  Decision Trees_Oversampling_Median Imputation_...  
 22  Decision Trees_Oversampling_Median Imputation_...  
 23  Decision Trees_Oversampling_Median Imputation_...  
 24  Decision Trees_Oversampling_Median Imputation_...  
 25  Decision Trees_Oversampling_Median Imputation_...  
 26  Decision Trees_Oversampling_Median Imputation_...  
 27  Decision Trees_Oversampling_Median Imputation_...  
 28  Decision Trees_Oversampling_Median Imputation_...  
 29  Decision Trees_Oversampling_Median Imputation_...  
 30  Decision Trees_Oversampling_Median Imputation_...  
 31  Decision Trees_Oversampling_Median Imputation_...  
 32  Decision Trees_Oversampling_Median Imputation_...  
 33  Decision Trees_Oversampling_Median Imputation_...  
 34  Decision Trees_Oversampling_Median Imputation_...  
 35  Decision Trees_Oversampling_Median Imputation_...  
 36  Decision Trees_Oversampling_Median Imputation_...  
 37  Decision Trees_Oversampling_Median Imputation_...  
 38  Decision Trees_Oversampling_Median Imputation_...  
 39  Decision Trees_Oversampling_Median Imputation_...  
 40  Decision Trees_Oversampling_Median Imputation_...  
 41  Decision Trees_Oversampling_Median Imputation_...  
 42  Decision Trees_Oversampling_Median Imputation_...  
 43  Decision Trees_Oversampling_Median Imputation_...  
 44  Decision Trees_Oversampling_Median Imputation_...  
 45  Decision Trees_Oversampling_Median Imputation_...  
 46  Decision Trees_Oversampling_Median Imputation_...  
 47  Decision Trees_Oversampling_Median Imputation_...  
 48  Decision Trees_Oversampling_Median Imputation_...  
 49  Decision Trees_Oversampling_Median Imputation_...  
 50  Decision Trees_Oversampling_Median Imputation_...  
 51  Decision Trees_Oversampling_Median Imputation_...  
 52  Decision Trees_Oversampling_Median Imputation_...  
 53  Decision Trees_Oversampling_Median Imputation_...  
 54  Decision Trees_Oversampling_Median Imputation_...  
 55  Decision Trees_Oversampling_Median Imputation_...  
 56  Decision Trees_Oversampling_Median Imputation_...  
 57  Decision Trees_Oversampling_Median Imputation_...  
 58  Decision Trees_Oversampling_Median Imputation_...  
 59  Decision Trees_Oversampling_Median Imputation_...  
 60  Decision Trees_Oversampling_Median Imputation_...  
 61  Decision Trees_Oversampling_Median Imputation_...  
 62  Decision Trees_Oversampling_Median Imputation_...  
 63  Decision Trees_Oversampling_Median Imputation_...  
 64  Decision Trees_Oversampling_Median Imputation_...  
 65  Decision Trees_Oversampling_Median Imputation_...  
 66  Decision Trees_Oversampling_Median Imputation_...  
 67  Decision Trees_Oversampling_Median Imputation_...  
 68  Decision Trees_Oversampling_Median Imputation_...  
 69  Decision Trees_Oversampling_Median Imputation_...  
 70  Decision Trees_Oversampling_Median Imputation_...  
 71  Decision Trees_Oversampling_Median Imputation_...  
 72  Decision Trees_Oversampling_Median Imputation_...  
 73  Decision Trees_Oversampling_Median Imputation_...  
 74  Decision Trees_Oversampling_Median Imputation_...  
 75  Decision Trees_Oversampling_Median Imputation_...  
 76  Decision Trees_Oversampling_Median Imputation_...  
 77  Decision Trees_Oversampling_Median Imputation_...  
 78  Decision Trees_Oversampling_Median Imputation_...  
 79  Decision Trees_Oversampling_Median Imputation_...  
 80  Decision Trees_Oversampling_Median Imputation_...  
 81  Decision Trees_Oversampling_Median Imputation_...  
 82  Decision Trees_Oversampling_Median Imputation_...  
 83  Decision Trees_Oversampling_Median Imputation_...  
 84  Decision Trees_Oversampling_Median Imputation_...  
 85  Decision Trees_Oversampling_Median Imputation_...  
 86  Decision Trees_Oversampling_Median Imputation_...  
 87  Decision Trees_Oversampling_Median Imputation_...  
 88  Decision Trees_Oversampling_Median Imputation_...  
 89  Decision Trees_Oversampling_Median Imputation_...  
 90  Decision Trees_Oversampling_Median Imputation_...  
 91  Decision Trees_Oversampling_Median Imputation_...  
 92  Decision Trees_Oversampling_Median Imputation_...  
 93  Decision Trees_Oversampling_Median Imputation_...  
 94  Decision Trees_Oversampling_Median Imputation_...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.571767             NaN      Zero Imputation   
 1   Decision Trees  0.080363             NaN      Zero Imputation   
 2   Decision Trees  0.561181             NaN      Zero Imputation   
 3   Decision Trees  0.140592             NaN      Zero Imputation   
 4   Decision Trees  0.562283             NaN      Zero Imputation   
 5   Decision Trees  0.136187             NaN      Zero Imputation   
 6   Decision Trees  0.124566             NaN      Zero Imputation   
 7   Decision Trees       NaN             0.0      Zero Imputation   
 8   Decision Trees       NaN            None      Zero Imputation   
 9   Decision Trees       NaN            gini      Zero Imputation   
 10  Decision Trees       NaN            None      Zero Imputation   
 11  Decision Trees       NaN            None      Zero Imputation   
 12  Decision Trees       NaN            None      Zero Imputation   
 13  Decision Trees       NaN             0.0      Zero Imputation   
 14  Decision Trees       NaN               1      Zero Imputation   
 15  Decision Trees       NaN               2      Zero Imputation   
 16  Decision Trees       NaN             0.0      Zero Imputation   
 17  Decision Trees       NaN            None      Zero Imputation   
 18  Decision Trees       NaN            best      Zero Imputation   
 19  Decision Trees  0.570977             NaN      Zero Imputation   
 20  Decision Trees  0.058469             NaN      Zero Imputation   
 21  Decision Trees  0.591463             NaN      Zero Imputation   
 22  Decision Trees  0.106418             NaN      Zero Imputation   
 23  Decision Trees  0.580208             NaN      Zero Imputation   
 24  Decision Trees  0.174028             NaN      Zero Imputation   
 25  Decision Trees  0.160416             NaN      Zero Imputation   
 26  Decision Trees       NaN             0.0      Zero Imputation   
 27  Decision Trees       NaN            None      Zero Imputation   
 28  Decision Trees       NaN            gini      Zero Imputation   
 29  Decision Trees       NaN            None      Zero Imputation   
 30  Decision Trees       NaN            None      Zero Imputation   
 31  Decision Trees       NaN            None      Zero Imputation   
 32  Decision Trees       NaN             0.0      Zero Imputation   
 33  Decision Trees       NaN               1      Zero Imputation   
 34  Decision Trees       NaN               2      Zero Imputation   
 35  Decision Trees       NaN             0.0      Zero Imputation   
 36  Decision Trees       NaN            None      Zero Imputation   
 37  Decision Trees       NaN            best      Zero Imputation   
 38  Decision Trees  0.538320             NaN      Zero Imputation   
 39  Decision Trees  0.060606             NaN      Zero Imputation   
 40  Decision Trees  0.577540             NaN      Zero Imputation   
 41  Decision Trees  0.109700             NaN      Zero Imputation   
 42  Decision Trees  0.547375             NaN      Zero Imputation   
 43  Decision Trees  0.115575             NaN      Zero Imputation   
 44  Decision Trees  0.094750             NaN      Zero Imputation   
 45  Decision Trees       NaN             0.0      Zero Imputation   
 46  Decision Trees       NaN            None      Zero Imputation   
 47  Decision Trees       NaN            gini      Zero Imputation   
 48  Decision Trees       NaN            None      Zero Imputation   
 49  Decision Trees       NaN            None      Zero Imputation   
 50  Decision Trees       NaN            None      Zero Imputation   
 51  Decision Trees       NaN             0.0      Zero Imputation   
 52  Decision Trees       NaN               1      Zero Imputation   
 53  Decision Trees       NaN               2      Zero Imputation   
 54  Decision Trees       NaN             0.0      Zero Imputation   
 55  Decision Trees       NaN            None      Zero Imputation   
 56  Decision Trees       NaN            best      Zero Imputation   
 57  Decision Trees  0.573498             NaN      Zero Imputation   
 58  Decision Trees  0.068526             NaN      Zero Imputation   
 59  Decision Trees  0.576531             NaN      Zero Imputation   
 60  Decision Trees  0.122493             NaN      Zero Imputation   
 61  Decision Trees  0.572456             NaN      Zero Imputation   
 62  Decision Trees  0.153226             NaN      Zero Imputation   
 63  Decision Trees  0.144912             NaN      Zero Imputation   
 64  Decision Trees       NaN             0.0      Zero Imputation   
 65  Decision Trees       NaN            None      Zero Imputation   
 66  Decision Trees       NaN            gini      Zero Imputation   
 67  Decision Trees       NaN            None      Zero Imputation   
 68  Decision Trees       NaN            None      Zero Imputation   
 69  Decision Trees       NaN            None      Zero Imputation   
 70  Decision Trees       NaN             0.0      Zero Imputation   
 71  Decision Trees       NaN               1      Zero Imputation   
 72  Decision Trees       NaN               2      Zero Imputation   
 73  Decision Trees       NaN             0.0      Zero Imputation   
 74  Decision Trees       NaN            None      Zero Imputation   
 75  Decision Trees       NaN            best      Zero Imputation   
 76  Decision Trees  0.577713             NaN      Zero Imputation   
 77  Decision Trees  0.077392             NaN      Zero Imputation   
 78  Decision Trees  0.587963             NaN      Zero Imputation   
 79  Decision Trees  0.136780             NaN      Zero Imputation   
 80  Decision Trees  0.578050             NaN      Zero Imputation   
 81  Decision Trees  0.165058             NaN      Zero Imputation   
 82  Decision Trees  0.156100             NaN      Zero Imputation   
 83  Decision Trees       NaN             0.0      Zero Imputation   
 84  Decision Trees       NaN            None      Zero Imputation   
 85  Decision Trees       NaN            gini      Zero Imputation   
 86  Decision Trees       NaN            None      Zero Imputation   
 87  Decision Trees       NaN            None      Zero Imputation   
 88  Decision Trees       NaN            None      Zero Imputation   
 89  Decision Trees       NaN             0.0      Zero Imputation   
 90  Decision Trees       NaN               1      Zero Imputation   
 91  Decision Trees       NaN               2      Zero Imputation   
 92  Decision Trees       NaN             0.0      Zero Imputation   
 93  Decision Trees       NaN            None      Zero Imputation   
 94  Decision Trees       NaN            best      Zero Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 75             Undersampling   
 76             Undersampling   
 77             Undersampling   
 78             Undersampling   
 79             Undersampling   
 80             Undersampling   
 81             Undersampling   
 82             Undersampling   
 83             Undersampling   
 84             Undersampling   
 85             Undersampling   
 86             Undersampling   
 87             Undersampling   
 88             Undersampling   
 89             Undersampling   
 90             Undersampling   
 91             Undersampling   
 92             Undersampling   
 93             Undersampling   
 94             Undersampling   
 
                                     Model Unique Code  
 0   Decision Trees_Undersampling_Zero Imputation_f...  
 1   Decision Trees_Undersampling_Zero Imputation_f...  
 2   Decision Trees_Undersampling_Zero Imputation_f...  
 3   Decision Trees_Undersampling_Zero Imputation_f...  
 4   Decision Trees_Undersampling_Zero Imputation_f...  
 5   Decision Trees_Undersampling_Zero Imputation_f...  
 6   Decision Trees_Undersampling_Zero Imputation_f...  
 7   Decision Trees_Undersampling_Zero Imputation_f...  
 8   Decision Trees_Undersampling_Zero Imputation_f...  
 9   Decision Trees_Undersampling_Zero Imputation_f...  
 10  Decision Trees_Undersampling_Zero Imputation_f...  
 11  Decision Trees_Undersampling_Zero Imputation_f...  
 12  Decision Trees_Undersampling_Zero Imputation_f...  
 13  Decision Trees_Undersampling_Zero Imputation_f...  
 14  Decision Trees_Undersampling_Zero Imputation_f...  
 15  Decision Trees_Undersampling_Zero Imputation_f...  
 16  Decision Trees_Undersampling_Zero Imputation_f...  
 17  Decision Trees_Undersampling_Zero Imputation_f...  
 18  Decision Trees_Undersampling_Zero Imputation_f...  
 19  Decision Trees_Undersampling_Zero Imputation_f...  
 20  Decision Trees_Undersampling_Zero Imputation_f...  
 21  Decision Trees_Undersampling_Zero Imputation_f...  
 22  Decision Trees_Undersampling_Zero Imputation_f...  
 23  Decision Trees_Undersampling_Zero Imputation_f...  
 24  Decision Trees_Undersampling_Zero Imputation_f...  
 25  Decision Trees_Undersampling_Zero Imputation_f...  
 26  Decision Trees_Undersampling_Zero Imputation_f...  
 27  Decision Trees_Undersampling_Zero Imputation_f...  
 28  Decision Trees_Undersampling_Zero Imputation_f...  
 29  Decision Trees_Undersampling_Zero Imputation_f...  
 30  Decision Trees_Undersampling_Zero Imputation_f...  
 31  Decision Trees_Undersampling_Zero Imputation_f...  
 32  Decision Trees_Undersampling_Zero Imputation_f...  
 33  Decision Trees_Undersampling_Zero Imputation_f...  
 34  Decision Trees_Undersampling_Zero Imputation_f...  
 35  Decision Trees_Undersampling_Zero Imputation_f...  
 36  Decision Trees_Undersampling_Zero Imputation_f...  
 37  Decision Trees_Undersampling_Zero Imputation_f...  
 38  Decision Trees_Undersampling_Zero Imputation_f...  
 39  Decision Trees_Undersampling_Zero Imputation_f...  
 40  Decision Trees_Undersampling_Zero Imputation_f...  
 41  Decision Trees_Undersampling_Zero Imputation_f...  
 42  Decision Trees_Undersampling_Zero Imputation_f...  
 43  Decision Trees_Undersampling_Zero Imputation_f...  
 44  Decision Trees_Undersampling_Zero Imputation_f...  
 45  Decision Trees_Undersampling_Zero Imputation_f...  
 46  Decision Trees_Undersampling_Zero Imputation_f...  
 47  Decision Trees_Undersampling_Zero Imputation_f...  
 48  Decision Trees_Undersampling_Zero Imputation_f...  
 49  Decision Trees_Undersampling_Zero Imputation_f...  
 50  Decision Trees_Undersampling_Zero Imputation_f...  
 51  Decision Trees_Undersampling_Zero Imputation_f...  
 52  Decision Trees_Undersampling_Zero Imputation_f...  
 53  Decision Trees_Undersampling_Zero Imputation_f...  
 54  Decision Trees_Undersampling_Zero Imputation_f...  
 55  Decision Trees_Undersampling_Zero Imputation_f...  
 56  Decision Trees_Undersampling_Zero Imputation_f...  
 57  Decision Trees_Undersampling_Zero Imputation_f...  
 58  Decision Trees_Undersampling_Zero Imputation_f...  
 59  Decision Trees_Undersampling_Zero Imputation_f...  
 60  Decision Trees_Undersampling_Zero Imputation_f...  
 61  Decision Trees_Undersampling_Zero Imputation_f...  
 62  Decision Trees_Undersampling_Zero Imputation_f...  
 63  Decision Trees_Undersampling_Zero Imputation_f...  
 64  Decision Trees_Undersampling_Zero Imputation_f...  
 65  Decision Trees_Undersampling_Zero Imputation_f...  
 66  Decision Trees_Undersampling_Zero Imputation_f...  
 67  Decision Trees_Undersampling_Zero Imputation_f...  
 68  Decision Trees_Undersampling_Zero Imputation_f...  
 69  Decision Trees_Undersampling_Zero Imputation_f...  
 70  Decision Trees_Undersampling_Zero Imputation_f...  
 71  Decision Trees_Undersampling_Zero Imputation_f...  
 72  Decision Trees_Undersampling_Zero Imputation_f...  
 73  Decision Trees_Undersampling_Zero Imputation_f...  
 74  Decision Trees_Undersampling_Zero Imputation_f...  
 75  Decision Trees_Undersampling_Zero Imputation_f...  
 76  Decision Trees_Undersampling_Zero Imputation_f...  
 77  Decision Trees_Undersampling_Zero Imputation_f...  
 78  Decision Trees_Undersampling_Zero Imputation_f...  
 79  Decision Trees_Undersampling_Zero Imputation_f...  
 80  Decision Trees_Undersampling_Zero Imputation_f...  
 81  Decision Trees_Undersampling_Zero Imputation_f...  
 82  Decision Trees_Undersampling_Zero Imputation_f...  
 83  Decision Trees_Undersampling_Zero Imputation_f...  
 84  Decision Trees_Undersampling_Zero Imputation_f...  
 85  Decision Trees_Undersampling_Zero Imputation_f...  
 86  Decision Trees_Undersampling_Zero Imputation_f...  
 87  Decision Trees_Undersampling_Zero Imputation_f...  
 88  Decision Trees_Undersampling_Zero Imputation_f...  
 89  Decision Trees_Undersampling_Zero Imputation_f...  
 90  Decision Trees_Undersampling_Zero Imputation_f...  
 91  Decision Trees_Undersampling_Zero Imputation_f...  
 92  Decision Trees_Undersampling_Zero Imputation_f...  
 93  Decision Trees_Undersampling_Zero Imputation_f...  
 94  Decision Trees_Undersampling_Zero Imputation_f...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.569397             NaN      Mean Imputation   
 1   Decision Trees  0.069581             NaN      Mean Imputation   
 2   Decision Trees  0.476793             NaN      Mean Imputation   
 3   Decision Trees  0.121440             NaN      Mean Imputation   
 4   Decision Trees  0.533647             NaN      Mean Imputation   
 5   Decision Trees  0.075430             NaN      Mean Imputation   
 6   Decision Trees  0.067295             NaN      Mean Imputation   
 7   Decision Trees       NaN             0.0      Mean Imputation   
 8   Decision Trees       NaN            None      Mean Imputation   
 9   Decision Trees       NaN            gini      Mean Imputation   
 10  Decision Trees       NaN            None      Mean Imputation   
 11  Decision Trees       NaN            None      Mean Imputation   
 12  Decision Trees       NaN            None      Mean Imputation   
 13  Decision Trees       NaN             0.0      Mean Imputation   
 14  Decision Trees       NaN               1      Mean Imputation   
 15  Decision Trees       NaN               2      Mean Imputation   
 16  Decision Trees       NaN             0.0      Mean Imputation   
 17  Decision Trees       NaN            None      Mean Imputation   
 18  Decision Trees       NaN            best      Mean Imputation   
 19  Decision Trees  0.568343             NaN      Mean Imputation   
 20  Decision Trees  0.058119             NaN      Mean Imputation   
 21  Decision Trees  0.591463             NaN      Mean Imputation   
 22  Decision Trees  0.105837             NaN      Mean Imputation   
 23  Decision Trees  0.579129             NaN      Mean Imputation   
 24  Decision Trees  0.168672             NaN      Mean Imputation   
 25  Decision Trees  0.158258             NaN      Mean Imputation   
 26  Decision Trees       NaN             0.0      Mean Imputation   
 27  Decision Trees       NaN            None      Mean Imputation   
 28  Decision Trees       NaN            gini      Mean Imputation   
 29  Decision Trees       NaN            None      Mean Imputation   
 30  Decision Trees       NaN            None      Mean Imputation   
 31  Decision Trees       NaN            None      Mean Imputation   
 32  Decision Trees       NaN             0.0      Mean Imputation   
 33  Decision Trees       NaN               1      Mean Imputation   
 34  Decision Trees       NaN               2      Mean Imputation   
 35  Decision Trees       NaN             0.0      Mean Imputation   
 36  Decision Trees       NaN            None      Mean Imputation   
 37  Decision Trees       NaN            best      Mean Imputation   
 38  Decision Trees  0.575718             NaN      Mean Imputation   
 39  Decision Trees  0.070567             NaN      Mean Imputation   
 40  Decision Trees  0.625668             NaN      Mean Imputation   
 41  Decision Trees  0.126829             NaN      Mean Imputation   
 42  Decision Trees  0.592935             NaN      Mean Imputation   
 43  Decision Trees  0.214649             NaN      Mean Imputation   
 44  Decision Trees  0.185870             NaN      Mean Imputation   
 45  Decision Trees       NaN             0.0      Mean Imputation   
 46  Decision Trees       NaN            None      Mean Imputation   
 47  Decision Trees       NaN            gini      Mean Imputation   
 48  Decision Trees       NaN            None      Mean Imputation   
 49  Decision Trees       NaN            None      Mean Imputation   
 50  Decision Trees       NaN            None      Mean Imputation   
 51  Decision Trees       NaN             0.0      Mean Imputation   
 52  Decision Trees       NaN               1      Mean Imputation   
 53  Decision Trees       NaN               2      Mean Imputation   
 54  Decision Trees       NaN             0.0      Mean Imputation   
 55  Decision Trees       NaN            None      Mean Imputation   
 56  Decision Trees       NaN            best      Mean Imputation   
 57  Decision Trees  0.560590             NaN      Mean Imputation   
 58  Decision Trees  0.066549             NaN      Mean Imputation   
 59  Decision Trees  0.576531             NaN      Mean Imputation   
 60  Decision Trees  0.119324             NaN      Mean Imputation   
 61  Decision Trees  0.562394             NaN      Mean Imputation   
 62  Decision Trees  0.140068             NaN      Mean Imputation   
 63  Decision Trees  0.124787             NaN      Mean Imputation   
 64  Decision Trees       NaN             0.0      Mean Imputation   
 65  Decision Trees       NaN            None      Mean Imputation   
 66  Decision Trees       NaN            gini      Mean Imputation   
 67  Decision Trees       NaN            None      Mean Imputation   
 68  Decision Trees       NaN            None      Mean Imputation   
 69  Decision Trees       NaN            None      Mean Imputation   
 70  Decision Trees       NaN             0.0      Mean Imputation   
 71  Decision Trees       NaN               1      Mean Imputation   
 72  Decision Trees       NaN               2      Mean Imputation   
 73  Decision Trees       NaN             0.0      Mean Imputation   
 74  Decision Trees       NaN            None      Mean Imputation   
 75  Decision Trees       NaN            best      Mean Imputation   
 76  Decision Trees  0.582455             NaN      Mean Imputation   
 77  Decision Trees  0.076685             NaN      Mean Imputation   
 78  Decision Trees  0.574074             NaN      Mean Imputation   
 79  Decision Trees  0.135297             NaN      Mean Imputation   
 80  Decision Trees  0.579621             NaN      Mean Imputation   
 81  Decision Trees  0.178109             NaN      Mean Imputation   
 82  Decision Trees  0.159241             NaN      Mean Imputation   
 83  Decision Trees       NaN             0.0      Mean Imputation   
 84  Decision Trees       NaN            None      Mean Imputation   
 85  Decision Trees       NaN            gini      Mean Imputation   
 86  Decision Trees       NaN            None      Mean Imputation   
 87  Decision Trees       NaN            None      Mean Imputation   
 88  Decision Trees       NaN            None      Mean Imputation   
 89  Decision Trees       NaN             0.0      Mean Imputation   
 90  Decision Trees       NaN               1      Mean Imputation   
 91  Decision Trees       NaN               2      Mean Imputation   
 92  Decision Trees       NaN             0.0      Mean Imputation   
 93  Decision Trees       NaN            None      Mean Imputation   
 94  Decision Trees       NaN            best      Mean Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 75             Undersampling   
 76             Undersampling   
 77             Undersampling   
 78             Undersampling   
 79             Undersampling   
 80             Undersampling   
 81             Undersampling   
 82             Undersampling   
 83             Undersampling   
 84             Undersampling   
 85             Undersampling   
 86             Undersampling   
 87             Undersampling   
 88             Undersampling   
 89             Undersampling   
 90             Undersampling   
 91             Undersampling   
 92             Undersampling   
 93             Undersampling   
 94             Undersampling   
 
                                     Model Unique Code  
 0   Decision Trees_Undersampling_Mean Imputation_f...  
 1   Decision Trees_Undersampling_Mean Imputation_f...  
 2   Decision Trees_Undersampling_Mean Imputation_f...  
 3   Decision Trees_Undersampling_Mean Imputation_f...  
 4   Decision Trees_Undersampling_Mean Imputation_f...  
 5   Decision Trees_Undersampling_Mean Imputation_f...  
 6   Decision Trees_Undersampling_Mean Imputation_f...  
 7   Decision Trees_Undersampling_Mean Imputation_f...  
 8   Decision Trees_Undersampling_Mean Imputation_f...  
 9   Decision Trees_Undersampling_Mean Imputation_f...  
 10  Decision Trees_Undersampling_Mean Imputation_f...  
 11  Decision Trees_Undersampling_Mean Imputation_f...  
 12  Decision Trees_Undersampling_Mean Imputation_f...  
 13  Decision Trees_Undersampling_Mean Imputation_f...  
 14  Decision Trees_Undersampling_Mean Imputation_f...  
 15  Decision Trees_Undersampling_Mean Imputation_f...  
 16  Decision Trees_Undersampling_Mean Imputation_f...  
 17  Decision Trees_Undersampling_Mean Imputation_f...  
 18  Decision Trees_Undersampling_Mean Imputation_f...  
 19  Decision Trees_Undersampling_Mean Imputation_f...  
 20  Decision Trees_Undersampling_Mean Imputation_f...  
 21  Decision Trees_Undersampling_Mean Imputation_f...  
 22  Decision Trees_Undersampling_Mean Imputation_f...  
 23  Decision Trees_Undersampling_Mean Imputation_f...  
 24  Decision Trees_Undersampling_Mean Imputation_f...  
 25  Decision Trees_Undersampling_Mean Imputation_f...  
 26  Decision Trees_Undersampling_Mean Imputation_f...  
 27  Decision Trees_Undersampling_Mean Imputation_f...  
 28  Decision Trees_Undersampling_Mean Imputation_f...  
 29  Decision Trees_Undersampling_Mean Imputation_f...  
 30  Decision Trees_Undersampling_Mean Imputation_f...  
 31  Decision Trees_Undersampling_Mean Imputation_f...  
 32  Decision Trees_Undersampling_Mean Imputation_f...  
 33  Decision Trees_Undersampling_Mean Imputation_f...  
 34  Decision Trees_Undersampling_Mean Imputation_f...  
 35  Decision Trees_Undersampling_Mean Imputation_f...  
 36  Decision Trees_Undersampling_Mean Imputation_f...  
 37  Decision Trees_Undersampling_Mean Imputation_f...  
 38  Decision Trees_Undersampling_Mean Imputation_f...  
 39  Decision Trees_Undersampling_Mean Imputation_f...  
 40  Decision Trees_Undersampling_Mean Imputation_f...  
 41  Decision Trees_Undersampling_Mean Imputation_f...  
 42  Decision Trees_Undersampling_Mean Imputation_f...  
 43  Decision Trees_Undersampling_Mean Imputation_f...  
 44  Decision Trees_Undersampling_Mean Imputation_f...  
 45  Decision Trees_Undersampling_Mean Imputation_f...  
 46  Decision Trees_Undersampling_Mean Imputation_f...  
 47  Decision Trees_Undersampling_Mean Imputation_f...  
 48  Decision Trees_Undersampling_Mean Imputation_f...  
 49  Decision Trees_Undersampling_Mean Imputation_f...  
 50  Decision Trees_Undersampling_Mean Imputation_f...  
 51  Decision Trees_Undersampling_Mean Imputation_f...  
 52  Decision Trees_Undersampling_Mean Imputation_f...  
 53  Decision Trees_Undersampling_Mean Imputation_f...  
 54  Decision Trees_Undersampling_Mean Imputation_f...  
 55  Decision Trees_Undersampling_Mean Imputation_f...  
 56  Decision Trees_Undersampling_Mean Imputation_f...  
 57  Decision Trees_Undersampling_Mean Imputation_f...  
 58  Decision Trees_Undersampling_Mean Imputation_f...  
 59  Decision Trees_Undersampling_Mean Imputation_f...  
 60  Decision Trees_Undersampling_Mean Imputation_f...  
 61  Decision Trees_Undersampling_Mean Imputation_f...  
 62  Decision Trees_Undersampling_Mean Imputation_f...  
 63  Decision Trees_Undersampling_Mean Imputation_f...  
 64  Decision Trees_Undersampling_Mean Imputation_f...  
 65  Decision Trees_Undersampling_Mean Imputation_f...  
 66  Decision Trees_Undersampling_Mean Imputation_f...  
 67  Decision Trees_Undersampling_Mean Imputation_f...  
 68  Decision Trees_Undersampling_Mean Imputation_f...  
 69  Decision Trees_Undersampling_Mean Imputation_f...  
 70  Decision Trees_Undersampling_Mean Imputation_f...  
 71  Decision Trees_Undersampling_Mean Imputation_f...  
 72  Decision Trees_Undersampling_Mean Imputation_f...  
 73  Decision Trees_Undersampling_Mean Imputation_f...  
 74  Decision Trees_Undersampling_Mean Imputation_f...  
 75  Decision Trees_Undersampling_Mean Imputation_f...  
 76  Decision Trees_Undersampling_Mean Imputation_f...  
 77  Decision Trees_Undersampling_Mean Imputation_f...  
 78  Decision Trees_Undersampling_Mean Imputation_f...  
 79  Decision Trees_Undersampling_Mean Imputation_f...  
 80  Decision Trees_Undersampling_Mean Imputation_f...  
 81  Decision Trees_Undersampling_Mean Imputation_f...  
 82  Decision Trees_Undersampling_Mean Imputation_f...  
 83  Decision Trees_Undersampling_Mean Imputation_f...  
 84  Decision Trees_Undersampling_Mean Imputation_f...  
 85  Decision Trees_Undersampling_Mean Imputation_f...  
 86  Decision Trees_Undersampling_Mean Imputation_f...  
 87  Decision Trees_Undersampling_Mean Imputation_f...  
 88  Decision Trees_Undersampling_Mean Imputation_f...  
 89  Decision Trees_Undersampling_Mean Imputation_f...  
 90  Decision Trees_Undersampling_Mean Imputation_f...  
 91  Decision Trees_Undersampling_Mean Imputation_f...  
 92  Decision Trees_Undersampling_Mean Imputation_f...  
 93  Decision Trees_Undersampling_Mean Imputation_f...  
 94  Decision Trees_Undersampling_Mean Imputation_f...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.570714             NaN    Median Imputation   
 1   Decision Trees  0.080674             NaN    Median Imputation   
 2   Decision Trees  0.565401             NaN    Median Imputation   
 3   Decision Trees  0.141201             NaN    Median Imputation   
 4   Decision Trees  0.565989             NaN    Median Imputation   
 5   Decision Trees  0.138154             NaN    Median Imputation   
 6   Decision Trees  0.131977             NaN    Median Imputation   
 7   Decision Trees       NaN             0.0    Median Imputation   
 8   Decision Trees       NaN            None    Median Imputation   
 9   Decision Trees       NaN            gini    Median Imputation   
 10  Decision Trees       NaN            None    Median Imputation   
 11  Decision Trees       NaN            None    Median Imputation   
 12  Decision Trees       NaN            None    Median Imputation   
 13  Decision Trees       NaN             0.0    Median Imputation   
 14  Decision Trees       NaN               1    Median Imputation   
 15  Decision Trees       NaN               2    Median Imputation   
 16  Decision Trees       NaN             0.0    Median Imputation   
 17  Decision Trees       NaN            None    Median Imputation   
 18  Decision Trees       NaN            best    Median Imputation   
 19  Decision Trees  0.569134             NaN    Median Imputation   
 20  Decision Trees  0.051766             NaN    Median Imputation   
 21  Decision Trees  0.518293             NaN    Median Imputation   
 22  Decision Trees  0.094131             NaN    Median Imputation   
 23  Decision Trees  0.550633             NaN    Median Imputation   
 24  Decision Trees  0.125798             NaN    Median Imputation   
 25  Decision Trees  0.101265             NaN    Median Imputation   
 26  Decision Trees       NaN             0.0    Median Imputation   
 27  Decision Trees       NaN            None    Median Imputation   
 28  Decision Trees       NaN            gini    Median Imputation   
 29  Decision Trees       NaN            None    Median Imputation   
 30  Decision Trees       NaN            None    Median Imputation   
 31  Decision Trees       NaN            None    Median Imputation   
 32  Decision Trees       NaN             0.0    Median Imputation   
 33  Decision Trees       NaN               1    Median Imputation   
 34  Decision Trees       NaN               2    Median Imputation   
 35  Decision Trees       NaN             0.0    Median Imputation   
 36  Decision Trees       NaN            None    Median Imputation   
 37  Decision Trees       NaN            best    Median Imputation   
 38  Decision Trees  0.572031             NaN    Median Imputation   
 39  Decision Trees  0.063714             NaN    Median Imputation   
 40  Decision Trees  0.561497             NaN    Median Imputation   
 41  Decision Trees  0.114441             NaN    Median Imputation   
 42  Decision Trees  0.549744             NaN    Median Imputation   
 43  Decision Trees  0.134074             NaN    Median Imputation   
 44  Decision Trees  0.099489             NaN    Median Imputation   
 45  Decision Trees       NaN             0.0    Median Imputation   
 46  Decision Trees       NaN            None    Median Imputation   
 47  Decision Trees       NaN            gini    Median Imputation   
 48  Decision Trees       NaN            None    Median Imputation   
 49  Decision Trees       NaN            None    Median Imputation   
 50  Decision Trees       NaN            None    Median Imputation   
 51  Decision Trees       NaN             0.0    Median Imputation   
 52  Decision Trees       NaN               1    Median Imputation   
 53  Decision Trees       NaN               2    Median Imputation   
 54  Decision Trees       NaN             0.0    Median Imputation   
 55  Decision Trees       NaN            None    Median Imputation   
 56  Decision Trees       NaN            best    Median Imputation   
 57  Decision Trees  0.561117             NaN    Median Imputation   
 58  Decision Trees  0.067647             NaN    Median Imputation   
 59  Decision Trees  0.586735             NaN    Median Imputation   
 60  Decision Trees  0.121308             NaN    Median Imputation   
 61  Decision Trees  0.568175             NaN    Median Imputation   
 62  Decision Trees  0.146457             NaN    Median Imputation   
 63  Decision Trees  0.136349             NaN    Median Imputation   
 64  Decision Trees       NaN             0.0    Median Imputation   
 65  Decision Trees       NaN            None    Median Imputation   
 66  Decision Trees       NaN            gini    Median Imputation   
 67  Decision Trees       NaN            None    Median Imputation   
 68  Decision Trees       NaN            None    Median Imputation   
 69  Decision Trees       NaN            None    Median Imputation   
 70  Decision Trees       NaN             0.0    Median Imputation   
 71  Decision Trees       NaN               1    Median Imputation   
 72  Decision Trees       NaN               2    Median Imputation   
 73  Decision Trees       NaN             0.0    Median Imputation   
 74  Decision Trees       NaN            None    Median Imputation   
 75  Decision Trees       NaN            best    Median Imputation   
 76  Decision Trees  0.566649             NaN    Median Imputation   
 77  Decision Trees  0.080446             NaN    Median Imputation   
 78  Decision Trees  0.634259             NaN    Median Imputation   
 79  Decision Trees  0.142783             NaN    Median Imputation   
 80  Decision Trees  0.596461             NaN    Median Imputation   
 81  Decision Trees  0.205411             NaN    Median Imputation   
 82  Decision Trees  0.192922             NaN    Median Imputation   
 83  Decision Trees       NaN             0.0    Median Imputation   
 84  Decision Trees       NaN            None    Median Imputation   
 85  Decision Trees       NaN            gini    Median Imputation   
 86  Decision Trees       NaN            None    Median Imputation   
 87  Decision Trees       NaN            None    Median Imputation   
 88  Decision Trees       NaN            None    Median Imputation   
 89  Decision Trees       NaN             0.0    Median Imputation   
 90  Decision Trees       NaN               1    Median Imputation   
 91  Decision Trees       NaN               2    Median Imputation   
 92  Decision Trees       NaN             0.0    Median Imputation   
 93  Decision Trees       NaN            None    Median Imputation   
 94  Decision Trees       NaN            best    Median Imputation   
 
    Imbalance Class Technique  \
 0              Undersampling   
 1              Undersampling   
 2              Undersampling   
 3              Undersampling   
 4              Undersampling   
 5              Undersampling   
 6              Undersampling   
 7              Undersampling   
 8              Undersampling   
 9              Undersampling   
 10             Undersampling   
 11             Undersampling   
 12             Undersampling   
 13             Undersampling   
 14             Undersampling   
 15             Undersampling   
 16             Undersampling   
 17             Undersampling   
 18             Undersampling   
 19             Undersampling   
 20             Undersampling   
 21             Undersampling   
 22             Undersampling   
 23             Undersampling   
 24             Undersampling   
 25             Undersampling   
 26             Undersampling   
 27             Undersampling   
 28             Undersampling   
 29             Undersampling   
 30             Undersampling   
 31             Undersampling   
 32             Undersampling   
 33             Undersampling   
 34             Undersampling   
 35             Undersampling   
 36             Undersampling   
 37             Undersampling   
 38             Undersampling   
 39             Undersampling   
 40             Undersampling   
 41             Undersampling   
 42             Undersampling   
 43             Undersampling   
 44             Undersampling   
 45             Undersampling   
 46             Undersampling   
 47             Undersampling   
 48             Undersampling   
 49             Undersampling   
 50             Undersampling   
 51             Undersampling   
 52             Undersampling   
 53             Undersampling   
 54             Undersampling   
 55             Undersampling   
 56             Undersampling   
 57             Undersampling   
 58             Undersampling   
 59             Undersampling   
 60             Undersampling   
 61             Undersampling   
 62             Undersampling   
 63             Undersampling   
 64             Undersampling   
 65             Undersampling   
 66             Undersampling   
 67             Undersampling   
 68             Undersampling   
 69             Undersampling   
 70             Undersampling   
 71             Undersampling   
 72             Undersampling   
 73             Undersampling   
 74             Undersampling   
 75             Undersampling   
 76             Undersampling   
 77             Undersampling   
 78             Undersampling   
 79             Undersampling   
 80             Undersampling   
 81             Undersampling   
 82             Undersampling   
 83             Undersampling   
 84             Undersampling   
 85             Undersampling   
 86             Undersampling   
 87             Undersampling   
 88             Undersampling   
 89             Undersampling   
 90             Undersampling   
 91             Undersampling   
 92             Undersampling   
 93             Undersampling   
 94             Undersampling   
 
                                     Model Unique Code  
 0   Decision Trees_Undersampling_Median Imputation...  
 1   Decision Trees_Undersampling_Median Imputation...  
 2   Decision Trees_Undersampling_Median Imputation...  
 3   Decision Trees_Undersampling_Median Imputation...  
 4   Decision Trees_Undersampling_Median Imputation...  
 5   Decision Trees_Undersampling_Median Imputation...  
 6   Decision Trees_Undersampling_Median Imputation...  
 7   Decision Trees_Undersampling_Median Imputation...  
 8   Decision Trees_Undersampling_Median Imputation...  
 9   Decision Trees_Undersampling_Median Imputation...  
 10  Decision Trees_Undersampling_Median Imputation...  
 11  Decision Trees_Undersampling_Median Imputation...  
 12  Decision Trees_Undersampling_Median Imputation...  
 13  Decision Trees_Undersampling_Median Imputation...  
 14  Decision Trees_Undersampling_Median Imputation...  
 15  Decision Trees_Undersampling_Median Imputation...  
 16  Decision Trees_Undersampling_Median Imputation...  
 17  Decision Trees_Undersampling_Median Imputation...  
 18  Decision Trees_Undersampling_Median Imputation...  
 19  Decision Trees_Undersampling_Median Imputation...  
 20  Decision Trees_Undersampling_Median Imputation...  
 21  Decision Trees_Undersampling_Median Imputation...  
 22  Decision Trees_Undersampling_Median Imputation...  
 23  Decision Trees_Undersampling_Median Imputation...  
 24  Decision Trees_Undersampling_Median Imputation...  
 25  Decision Trees_Undersampling_Median Imputation...  
 26  Decision Trees_Undersampling_Median Imputation...  
 27  Decision Trees_Undersampling_Median Imputation...  
 28  Decision Trees_Undersampling_Median Imputation...  
 29  Decision Trees_Undersampling_Median Imputation...  
 30  Decision Trees_Undersampling_Median Imputation...  
 31  Decision Trees_Undersampling_Median Imputation...  
 32  Decision Trees_Undersampling_Median Imputation...  
 33  Decision Trees_Undersampling_Median Imputation...  
 34  Decision Trees_Undersampling_Median Imputation...  
 35  Decision Trees_Undersampling_Median Imputation...  
 36  Decision Trees_Undersampling_Median Imputation...  
 37  Decision Trees_Undersampling_Median Imputation...  
 38  Decision Trees_Undersampling_Median Imputation...  
 39  Decision Trees_Undersampling_Median Imputation...  
 40  Decision Trees_Undersampling_Median Imputation...  
 41  Decision Trees_Undersampling_Median Imputation...  
 42  Decision Trees_Undersampling_Median Imputation...  
 43  Decision Trees_Undersampling_Median Imputation...  
 44  Decision Trees_Undersampling_Median Imputation...  
 45  Decision Trees_Undersampling_Median Imputation...  
 46  Decision Trees_Undersampling_Median Imputation...  
 47  Decision Trees_Undersampling_Median Imputation...  
 48  Decision Trees_Undersampling_Median Imputation...  
 49  Decision Trees_Undersampling_Median Imputation...  
 50  Decision Trees_Undersampling_Median Imputation...  
 51  Decision Trees_Undersampling_Median Imputation...  
 52  Decision Trees_Undersampling_Median Imputation...  
 53  Decision Trees_Undersampling_Median Imputation...  
 54  Decision Trees_Undersampling_Median Imputation...  
 55  Decision Trees_Undersampling_Median Imputation...  
 56  Decision Trees_Undersampling_Median Imputation...  
 57  Decision Trees_Undersampling_Median Imputation...  
 58  Decision Trees_Undersampling_Median Imputation...  
 59  Decision Trees_Undersampling_Median Imputation...  
 60  Decision Trees_Undersampling_Median Imputation...  
 61  Decision Trees_Undersampling_Median Imputation...  
 62  Decision Trees_Undersampling_Median Imputation...  
 63  Decision Trees_Undersampling_Median Imputation...  
 64  Decision Trees_Undersampling_Median Imputation...  
 65  Decision Trees_Undersampling_Median Imputation...  
 66  Decision Trees_Undersampling_Median Imputation...  
 67  Decision Trees_Undersampling_Median Imputation...  
 68  Decision Trees_Undersampling_Median Imputation...  
 69  Decision Trees_Undersampling_Median Imputation...  
 70  Decision Trees_Undersampling_Median Imputation...  
 71  Decision Trees_Undersampling_Median Imputation...  
 72  Decision Trees_Undersampling_Median Imputation...  
 73  Decision Trees_Undersampling_Median Imputation...  
 74  Decision Trees_Undersampling_Median Imputation...  
 75  Decision Trees_Undersampling_Median Imputation...  
 76  Decision Trees_Undersampling_Median Imputation...  
 77  Decision Trees_Undersampling_Median Imputation...  
 78  Decision Trees_Undersampling_Median Imputation...  
 79  Decision Trees_Undersampling_Median Imputation...  
 80  Decision Trees_Undersampling_Median Imputation...  
 81  Decision Trees_Undersampling_Median Imputation...  
 82  Decision Trees_Undersampling_Median Imputation...  
 83  Decision Trees_Undersampling_Median Imputation...  
 84  Decision Trees_Undersampling_Median Imputation...  
 85  Decision Trees_Undersampling_Median Imputation...  
 86  Decision Trees_Undersampling_Median Imputation...  
 87  Decision Trees_Undersampling_Median Imputation...  
 88  Decision Trees_Undersampling_Median Imputation...  
 89  Decision Trees_Undersampling_Median Imputation...  
 90  Decision Trees_Undersampling_Median Imputation...  
 91  Decision Trees_Undersampling_Median Imputation...  
 92  Decision Trees_Undersampling_Median Imputation...  
 93  Decision Trees_Undersampling_Median Imputation...  
 94  Decision Trees_Undersampling_Median Imputation...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.837767             NaN      Zero Imputation   
 1   Decision Trees  0.107660             NaN      Zero Imputation   
 2   Decision Trees  0.219409             NaN      Zero Imputation   
 3   Decision Trees  0.144444             NaN      Zero Imputation   
 4   Decision Trees  0.549171             NaN      Zero Imputation   
 5   Decision Trees  0.098342             NaN      Zero Imputation   
 6   Decision Trees  0.098342             NaN      Zero Imputation   
 7   Decision Trees       NaN             0.0      Zero Imputation   
 8   Decision Trees       NaN            None      Zero Imputation   
 9   Decision Trees       NaN            gini      Zero Imputation   
 10  Decision Trees       NaN            None      Zero Imputation   
 11  Decision Trees       NaN            None      Zero Imputation   
 12  Decision Trees       NaN            None      Zero Imputation   
 13  Decision Trees       NaN             0.0      Zero Imputation   
 14  Decision Trees       NaN               1      Zero Imputation   
 15  Decision Trees       NaN               2      Zero Imputation   
 16  Decision Trees       NaN             0.0      Zero Imputation   
 17  Decision Trees       NaN            None      Zero Imputation   
 18  Decision Trees       NaN            best      Zero Imputation   
 19  Decision Trees  0.828812             NaN      Zero Imputation   
 20  Decision Trees  0.078125             NaN      Zero Imputation   
 21  Decision Trees  0.274390             NaN      Zero Imputation   
 22  Decision Trees  0.121622             NaN      Zero Imputation   
 23  Decision Trees  0.564115             NaN      Zero Imputation   
 24  Decision Trees  0.128230             NaN      Zero Imputation   
 25  Decision Trees  0.128230             NaN      Zero Imputation   
 26  Decision Trees       NaN             0.0      Zero Imputation   
 27  Decision Trees       NaN            None      Zero Imputation   
 28  Decision Trees       NaN            gini      Zero Imputation   
 29  Decision Trees       NaN            None      Zero Imputation   
 30  Decision Trees       NaN            None      Zero Imputation   
 31  Decision Trees       NaN            None      Zero Imputation   
 32  Decision Trees       NaN             0.0      Zero Imputation   
 33  Decision Trees       NaN               1      Zero Imputation   
 34  Decision Trees       NaN               2      Zero Imputation   
 35  Decision Trees       NaN             0.0      Zero Imputation   
 36  Decision Trees       NaN            None      Zero Imputation   
 37  Decision Trees       NaN            best      Zero Imputation   
 38  Decision Trees  0.830919             NaN      Zero Imputation   
 39  Decision Trees  0.076350             NaN      Zero Imputation   
 40  Decision Trees  0.219251             NaN      Zero Imputation   
 41  Decision Trees  0.113260             NaN      Zero Imputation   
 42  Decision Trees  0.540928             NaN      Zero Imputation   
 43  Decision Trees  0.081855             NaN      Zero Imputation   
 44  Decision Trees  0.081855             NaN      Zero Imputation   
 45  Decision Trees       NaN             0.0      Zero Imputation   
 46  Decision Trees       NaN            None      Zero Imputation   
 47  Decision Trees       NaN            gini      Zero Imputation   
 48  Decision Trees       NaN            None      Zero Imputation   
 49  Decision Trees       NaN            None      Zero Imputation   
 50  Decision Trees       NaN            None      Zero Imputation   
 51  Decision Trees       NaN             0.0      Zero Imputation   
 52  Decision Trees       NaN               1      Zero Imputation   
 53  Decision Trees       NaN               2      Zero Imputation   
 54  Decision Trees       NaN             0.0      Zero Imputation   
 55  Decision Trees       NaN            None      Zero Imputation   
 56  Decision Trees       NaN            best      Zero Imputation   
 57  Decision Trees  0.843783             NaN      Zero Imputation   
 58  Decision Trees  0.100604             NaN      Zero Imputation   
 59  Decision Trees  0.255102             NaN      Zero Imputation   
 60  Decision Trees  0.144300             NaN      Zero Imputation   
 61  Decision Trees  0.565468             NaN      Zero Imputation   
 62  Decision Trees  0.130935             NaN      Zero Imputation   
 63  Decision Trees  0.130935             NaN      Zero Imputation   
 64  Decision Trees       NaN             0.0      Zero Imputation   
 65  Decision Trees       NaN            None      Zero Imputation   
 66  Decision Trees       NaN            gini      Zero Imputation   
 67  Decision Trees       NaN            None      Zero Imputation   
 68  Decision Trees       NaN            None      Zero Imputation   
 69  Decision Trees       NaN            None      Zero Imputation   
 70  Decision Trees       NaN             0.0      Zero Imputation   
 71  Decision Trees       NaN               1      Zero Imputation   
 72  Decision Trees       NaN               2      Zero Imputation   
 73  Decision Trees       NaN             0.0      Zero Imputation   
 74  Decision Trees       NaN            None      Zero Imputation   
 75  Decision Trees       NaN            best      Zero Imputation   
 76  Decision Trees  0.832719             NaN      Zero Imputation   
 77  Decision Trees  0.090020             NaN      Zero Imputation   
 78  Decision Trees  0.212963             NaN      Zero Imputation   
 79  Decision Trees  0.126547             NaN      Zero Imputation   
 80  Decision Trees  0.541537             NaN      Zero Imputation   
 81  Decision Trees  0.083075             NaN      Zero Imputation   
 82  Decision Trees  0.083075             NaN      Zero Imputation   
 83  Decision Trees       NaN             0.0      Zero Imputation   
 84  Decision Trees       NaN            None      Zero Imputation   
 85  Decision Trees       NaN            gini      Zero Imputation   
 86  Decision Trees       NaN            None      Zero Imputation   
 87  Decision Trees       NaN            None      Zero Imputation   
 88  Decision Trees       NaN            None      Zero Imputation   
 89  Decision Trees       NaN             0.0      Zero Imputation   
 90  Decision Trees       NaN               1      Zero Imputation   
 91  Decision Trees       NaN               2      Zero Imputation   
 92  Decision Trees       NaN             0.0      Zero Imputation   
 93  Decision Trees       NaN            None      Zero Imputation   
 94  Decision Trees       NaN            best      Zero Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 75  Hybrid Sampling (SMOTEENN)   
 76  Hybrid Sampling (SMOTEENN)   
 77  Hybrid Sampling (SMOTEENN)   
 78  Hybrid Sampling (SMOTEENN)   
 79  Hybrid Sampling (SMOTEENN)   
 80  Hybrid Sampling (SMOTEENN)   
 81  Hybrid Sampling (SMOTEENN)   
 82  Hybrid Sampling (SMOTEENN)   
 83  Hybrid Sampling (SMOTEENN)   
 84  Hybrid Sampling (SMOTEENN)   
 85  Hybrid Sampling (SMOTEENN)   
 86  Hybrid Sampling (SMOTEENN)   
 87  Hybrid Sampling (SMOTEENN)   
 88  Hybrid Sampling (SMOTEENN)   
 89  Hybrid Sampling (SMOTEENN)   
 90  Hybrid Sampling (SMOTEENN)   
 91  Hybrid Sampling (SMOTEENN)   
 92  Hybrid Sampling (SMOTEENN)   
 93  Hybrid Sampling (SMOTEENN)   
 94  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 1   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 2   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 3   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 4   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 5   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 6   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 7   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 8   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 9   Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 10  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 11  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 12  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 13  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 14  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 15  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 16  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 17  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 18  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 19  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 20  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 21  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 22  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 23  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 24  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 25  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 26  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 27  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 28  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 29  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 30  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 31  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 32  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 33  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 34  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 35  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 36  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 37  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 38  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 39  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 40  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 41  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 42  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 43  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 44  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 45  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 46  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 47  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 48  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 49  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 50  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 51  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 52  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 53  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 54  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 55  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 56  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 57  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 58  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 59  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 60  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 61  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 62  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 63  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 64  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 65  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 66  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 67  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 68  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 69  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 70  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 71  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 72  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 73  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 74  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 75  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 76  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 77  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 78  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 79  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 80  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 81  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 82  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 83  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 84  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 85  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 86  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 87  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 88  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 89  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 90  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 91  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 92  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 93  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  
 94  Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.838293             NaN      Mean Imputation   
 1   Decision Trees  0.108108             NaN      Mean Imputation   
 2   Decision Trees  0.219409             NaN      Mean Imputation   
 3   Decision Trees  0.144847             NaN      Mean Imputation   
 4   Decision Trees  0.549452             NaN      Mean Imputation   
 5   Decision Trees  0.098904             NaN      Mean Imputation   
 6   Decision Trees  0.098904             NaN      Mean Imputation   
 7   Decision Trees       NaN             0.0      Mean Imputation   
 8   Decision Trees       NaN            None      Mean Imputation   
 9   Decision Trees       NaN            gini      Mean Imputation   
 10  Decision Trees       NaN            None      Mean Imputation   
 11  Decision Trees       NaN            None      Mean Imputation   
 12  Decision Trees       NaN            None      Mean Imputation   
 13  Decision Trees       NaN             0.0      Mean Imputation   
 14  Decision Trees       NaN               1      Mean Imputation   
 15  Decision Trees       NaN               2      Mean Imputation   
 16  Decision Trees       NaN             0.0      Mean Imputation   
 17  Decision Trees       NaN            None      Mean Imputation   
 18  Decision Trees       NaN            best      Mean Imputation   
 19  Decision Trees  0.829076             NaN      Mean Imputation   
 20  Decision Trees  0.073814             NaN      Mean Imputation   
 21  Decision Trees  0.256098             NaN      Mean Imputation   
 22  Decision Trees  0.114598             NaN      Mean Imputation   
 23  Decision Trees  0.555519             NaN      Mean Imputation   
 24  Decision Trees  0.111038             NaN      Mean Imputation   
 25  Decision Trees  0.111038             NaN      Mean Imputation   
 26  Decision Trees       NaN             0.0      Mean Imputation   
 27  Decision Trees       NaN            None      Mean Imputation   
 28  Decision Trees       NaN            gini      Mean Imputation   
 29  Decision Trees       NaN            None      Mean Imputation   
 30  Decision Trees       NaN            None      Mean Imputation   
 31  Decision Trees       NaN            None      Mean Imputation   
 32  Decision Trees       NaN             0.0      Mean Imputation   
 33  Decision Trees       NaN               1      Mean Imputation   
 34  Decision Trees       NaN               2      Mean Imputation   
 35  Decision Trees       NaN             0.0      Mean Imputation   
 36  Decision Trees       NaN            None      Mean Imputation   
 37  Decision Trees       NaN            best      Mean Imputation   
 38  Decision Trees  0.835923             NaN      Mean Imputation   
 39  Decision Trees  0.072549             NaN      Mean Imputation   
 40  Decision Trees  0.197861             NaN      Mean Imputation   
 41  Decision Trees  0.106169             NaN      Mean Imputation   
 42  Decision Trees  0.533418             NaN      Mean Imputation   
 43  Decision Trees  0.066836             NaN      Mean Imputation   
 44  Decision Trees  0.066836             NaN      Mean Imputation   
 45  Decision Trees       NaN             0.0      Mean Imputation   
 46  Decision Trees       NaN            None      Mean Imputation   
 47  Decision Trees       NaN            gini      Mean Imputation   
 48  Decision Trees       NaN            None      Mean Imputation   
 49  Decision Trees       NaN            None      Mean Imputation   
 50  Decision Trees       NaN            None      Mean Imputation   
 51  Decision Trees       NaN             0.0      Mean Imputation   
 52  Decision Trees       NaN               1      Mean Imputation   
 53  Decision Trees       NaN               2      Mean Imputation   
 54  Decision Trees       NaN             0.0      Mean Imputation   
 55  Decision Trees       NaN            None      Mean Imputation   
 56  Decision Trees       NaN            best      Mean Imputation   
 57  Decision Trees  0.850632             NaN      Mean Imputation   
 58  Decision Trees  0.094092             NaN      Mean Imputation   
 59  Decision Trees  0.219388             NaN      Mean Imputation   
 60  Decision Trees  0.131700             NaN      Mean Imputation   
 61  Decision Trees  0.552194             NaN      Mean Imputation   
 62  Decision Trees  0.104388             NaN      Mean Imputation   
 63  Decision Trees  0.104388             NaN      Mean Imputation   
 64  Decision Trees       NaN             0.0      Mean Imputation   
 65  Decision Trees       NaN            None      Mean Imputation   
 66  Decision Trees       NaN            gini      Mean Imputation   
 67  Decision Trees       NaN            None      Mean Imputation   
 68  Decision Trees       NaN            None      Mean Imputation   
 69  Decision Trees       NaN            None      Mean Imputation   
 70  Decision Trees       NaN             0.0      Mean Imputation   
 71  Decision Trees       NaN               1      Mean Imputation   
 72  Decision Trees       NaN               2      Mean Imputation   
 73  Decision Trees       NaN             0.0      Mean Imputation   
 74  Decision Trees       NaN            None      Mean Imputation   
 75  Decision Trees       NaN            best      Mean Imputation   
 76  Decision Trees  0.831665             NaN      Mean Imputation   
 77  Decision Trees  0.098672             NaN      Mean Imputation   
 78  Decision Trees  0.240741             NaN      Mean Imputation   
 79  Decision Trees  0.139973             NaN      Mean Imputation   
 80  Decision Trees  0.554030             NaN      Mean Imputation   
 81  Decision Trees  0.108059             NaN      Mean Imputation   
 82  Decision Trees  0.108059             NaN      Mean Imputation   
 83  Decision Trees       NaN             0.0      Mean Imputation   
 84  Decision Trees       NaN            None      Mean Imputation   
 85  Decision Trees       NaN            gini      Mean Imputation   
 86  Decision Trees       NaN            None      Mean Imputation   
 87  Decision Trees       NaN            None      Mean Imputation   
 88  Decision Trees       NaN            None      Mean Imputation   
 89  Decision Trees       NaN             0.0      Mean Imputation   
 90  Decision Trees       NaN               1      Mean Imputation   
 91  Decision Trees       NaN               2      Mean Imputation   
 92  Decision Trees       NaN             0.0      Mean Imputation   
 93  Decision Trees       NaN            None      Mean Imputation   
 94  Decision Trees       NaN            best      Mean Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 75  Hybrid Sampling (SMOTEENN)   
 76  Hybrid Sampling (SMOTEENN)   
 77  Hybrid Sampling (SMOTEENN)   
 78  Hybrid Sampling (SMOTEENN)   
 79  Hybrid Sampling (SMOTEENN)   
 80  Hybrid Sampling (SMOTEENN)   
 81  Hybrid Sampling (SMOTEENN)   
 82  Hybrid Sampling (SMOTEENN)   
 83  Hybrid Sampling (SMOTEENN)   
 84  Hybrid Sampling (SMOTEENN)   
 85  Hybrid Sampling (SMOTEENN)   
 86  Hybrid Sampling (SMOTEENN)   
 87  Hybrid Sampling (SMOTEENN)   
 88  Hybrid Sampling (SMOTEENN)   
 89  Hybrid Sampling (SMOTEENN)   
 90  Hybrid Sampling (SMOTEENN)   
 91  Hybrid Sampling (SMOTEENN)   
 92  Hybrid Sampling (SMOTEENN)   
 93  Hybrid Sampling (SMOTEENN)   
 94  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 1   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 2   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 3   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 4   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 5   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 6   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 7   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 8   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 9   Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 10  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 11  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 12  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 13  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 14  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 15  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 16  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 17  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 18  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 19  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 20  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 21  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 22  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 23  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 24  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 25  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 26  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 27  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 28  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 29  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 30  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 31  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 32  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 33  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 34  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 35  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 36  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 37  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 38  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 39  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 40  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 41  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 42  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 43  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 44  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 45  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 46  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 47  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 48  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 49  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 50  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 51  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 52  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 53  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 54  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 55  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 56  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 57  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 58  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 59  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 60  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 61  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 62  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 63  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 64  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 65  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 66  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 67  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 68  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 69  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 70  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 71  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 72  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 73  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 74  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 75  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 76  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 77  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 78  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 79  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 80  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 81  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 82  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 83  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 84  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 85  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 86  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 87  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 88  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 89  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 90  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 91  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 92  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 93  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  
 94  Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.839083             NaN    Median Imputation   
 1   Decision Trees  0.103814             NaN    Median Imputation   
 2   Decision Trees  0.206751             NaN    Median Imputation   
 3   Decision Trees  0.138223             NaN    Median Imputation   
 4   Decision Trees  0.543965             NaN    Median Imputation   
 5   Decision Trees  0.087931             NaN    Median Imputation   
 6   Decision Trees  0.087931             NaN    Median Imputation   
 7   Decision Trees       NaN             0.0    Median Imputation   
 8   Decision Trees       NaN            None    Median Imputation   
 9   Decision Trees       NaN            gini    Median Imputation   
 10  Decision Trees       NaN            None    Median Imputation   
 11  Decision Trees       NaN            None    Median Imputation   
 12  Decision Trees       NaN            None    Median Imputation   
 13  Decision Trees       NaN             0.0    Median Imputation   
 14  Decision Trees       NaN               1    Median Imputation   
 15  Decision Trees       NaN               2    Median Imputation   
 16  Decision Trees       NaN             0.0    Median Imputation   
 17  Decision Trees       NaN            None    Median Imputation   
 18  Decision Trees       NaN            best    Median Imputation   
 19  Decision Trees  0.824072             NaN    Median Imputation   
 20  Decision Trees  0.056338             NaN    Median Imputation   
 21  Decision Trees  0.195122             NaN    Median Imputation   
 22  Decision Trees  0.087432             NaN    Median Imputation   
 23  Decision Trees  0.523793             NaN    Median Imputation   
 24  Decision Trees  0.047585             NaN    Median Imputation   
 25  Decision Trees  0.047585             NaN    Median Imputation   
 26  Decision Trees       NaN             0.0    Median Imputation   
 27  Decision Trees       NaN            None    Median Imputation   
 28  Decision Trees       NaN            gini    Median Imputation   
 29  Decision Trees       NaN            None    Median Imputation   
 30  Decision Trees       NaN            None    Median Imputation   
 31  Decision Trees       NaN            None    Median Imputation   
 32  Decision Trees       NaN             0.0    Median Imputation   
 33  Decision Trees       NaN               1    Median Imputation   
 34  Decision Trees       NaN               2    Median Imputation   
 35  Decision Trees       NaN             0.0    Median Imputation   
 36  Decision Trees       NaN            None    Median Imputation   
 37  Decision Trees       NaN            best    Median Imputation   
 38  Decision Trees  0.831446             NaN    Median Imputation   
 39  Decision Trees  0.079777             NaN    Median Imputation   
 40  Decision Trees  0.229947             NaN    Median Imputation   
 41  Decision Trees  0.118457             NaN    Median Imputation   
 42  Decision Trees  0.546275             NaN    Median Imputation   
 43  Decision Trees  0.092550             NaN    Median Imputation   
 44  Decision Trees  0.092550             NaN    Median Imputation   
 45  Decision Trees       NaN             0.0    Median Imputation   
 46  Decision Trees       NaN            None    Median Imputation   
 47  Decision Trees       NaN            gini    Median Imputation   
 48  Decision Trees       NaN            None    Median Imputation   
 49  Decision Trees       NaN            None    Median Imputation   
 50  Decision Trees       NaN            None    Median Imputation   
 51  Decision Trees       NaN             0.0    Median Imputation   
 52  Decision Trees       NaN               1    Median Imputation   
 53  Decision Trees       NaN               2    Median Imputation   
 54  Decision Trees       NaN             0.0    Median Imputation   
 55  Decision Trees       NaN            None    Median Imputation   
 56  Decision Trees       NaN            best    Median Imputation   
 57  Decision Trees  0.834036             NaN    Median Imputation   
 58  Decision Trees  0.085878             NaN    Median Imputation   
 59  Decision Trees  0.229592             NaN    Median Imputation   
 60  Decision Trees  0.125000             NaN    Median Imputation   
 61  Decision Trees  0.548268             NaN    Median Imputation   
 62  Decision Trees  0.096536             NaN    Median Imputation   
 63  Decision Trees  0.096536             NaN    Median Imputation   
 64  Decision Trees       NaN             0.0    Median Imputation   
 65  Decision Trees       NaN            None    Median Imputation   
 66  Decision Trees       NaN            gini    Median Imputation   
 67  Decision Trees       NaN            None    Median Imputation   
 68  Decision Trees       NaN            None    Median Imputation   
 69  Decision Trees       NaN            None    Median Imputation   
 70  Decision Trees       NaN             0.0    Median Imputation   
 71  Decision Trees       NaN               1    Median Imputation   
 72  Decision Trees       NaN               2    Median Imputation   
 73  Decision Trees       NaN             0.0    Median Imputation   
 74  Decision Trees       NaN            None    Median Imputation   
 75  Decision Trees       NaN            best    Median Imputation   
 76  Decision Trees  0.825079             NaN    Median Imputation   
 77  Decision Trees  0.089744             NaN    Median Imputation   
 78  Decision Trees  0.226852             NaN    Median Imputation   
 79  Decision Trees  0.128609             NaN    Median Imputation   
 80  Decision Trees  0.544013             NaN    Median Imputation   
 81  Decision Trees  0.088025             NaN    Median Imputation   
 82  Decision Trees  0.088025             NaN    Median Imputation   
 83  Decision Trees       NaN             0.0    Median Imputation   
 84  Decision Trees       NaN            None    Median Imputation   
 85  Decision Trees       NaN            gini    Median Imputation   
 86  Decision Trees       NaN            None    Median Imputation   
 87  Decision Trees       NaN            None    Median Imputation   
 88  Decision Trees       NaN            None    Median Imputation   
 89  Decision Trees       NaN             0.0    Median Imputation   
 90  Decision Trees       NaN               1    Median Imputation   
 91  Decision Trees       NaN               2    Median Imputation   
 92  Decision Trees       NaN             0.0    Median Imputation   
 93  Decision Trees       NaN            None    Median Imputation   
 94  Decision Trees       NaN            best    Median Imputation   
 
      Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTEENN)   
 1   Hybrid Sampling (SMOTEENN)   
 2   Hybrid Sampling (SMOTEENN)   
 3   Hybrid Sampling (SMOTEENN)   
 4   Hybrid Sampling (SMOTEENN)   
 5   Hybrid Sampling (SMOTEENN)   
 6   Hybrid Sampling (SMOTEENN)   
 7   Hybrid Sampling (SMOTEENN)   
 8   Hybrid Sampling (SMOTEENN)   
 9   Hybrid Sampling (SMOTEENN)   
 10  Hybrid Sampling (SMOTEENN)   
 11  Hybrid Sampling (SMOTEENN)   
 12  Hybrid Sampling (SMOTEENN)   
 13  Hybrid Sampling (SMOTEENN)   
 14  Hybrid Sampling (SMOTEENN)   
 15  Hybrid Sampling (SMOTEENN)   
 16  Hybrid Sampling (SMOTEENN)   
 17  Hybrid Sampling (SMOTEENN)   
 18  Hybrid Sampling (SMOTEENN)   
 19  Hybrid Sampling (SMOTEENN)   
 20  Hybrid Sampling (SMOTEENN)   
 21  Hybrid Sampling (SMOTEENN)   
 22  Hybrid Sampling (SMOTEENN)   
 23  Hybrid Sampling (SMOTEENN)   
 24  Hybrid Sampling (SMOTEENN)   
 25  Hybrid Sampling (SMOTEENN)   
 26  Hybrid Sampling (SMOTEENN)   
 27  Hybrid Sampling (SMOTEENN)   
 28  Hybrid Sampling (SMOTEENN)   
 29  Hybrid Sampling (SMOTEENN)   
 30  Hybrid Sampling (SMOTEENN)   
 31  Hybrid Sampling (SMOTEENN)   
 32  Hybrid Sampling (SMOTEENN)   
 33  Hybrid Sampling (SMOTEENN)   
 34  Hybrid Sampling (SMOTEENN)   
 35  Hybrid Sampling (SMOTEENN)   
 36  Hybrid Sampling (SMOTEENN)   
 37  Hybrid Sampling (SMOTEENN)   
 38  Hybrid Sampling (SMOTEENN)   
 39  Hybrid Sampling (SMOTEENN)   
 40  Hybrid Sampling (SMOTEENN)   
 41  Hybrid Sampling (SMOTEENN)   
 42  Hybrid Sampling (SMOTEENN)   
 43  Hybrid Sampling (SMOTEENN)   
 44  Hybrid Sampling (SMOTEENN)   
 45  Hybrid Sampling (SMOTEENN)   
 46  Hybrid Sampling (SMOTEENN)   
 47  Hybrid Sampling (SMOTEENN)   
 48  Hybrid Sampling (SMOTEENN)   
 49  Hybrid Sampling (SMOTEENN)   
 50  Hybrid Sampling (SMOTEENN)   
 51  Hybrid Sampling (SMOTEENN)   
 52  Hybrid Sampling (SMOTEENN)   
 53  Hybrid Sampling (SMOTEENN)   
 54  Hybrid Sampling (SMOTEENN)   
 55  Hybrid Sampling (SMOTEENN)   
 56  Hybrid Sampling (SMOTEENN)   
 57  Hybrid Sampling (SMOTEENN)   
 58  Hybrid Sampling (SMOTEENN)   
 59  Hybrid Sampling (SMOTEENN)   
 60  Hybrid Sampling (SMOTEENN)   
 61  Hybrid Sampling (SMOTEENN)   
 62  Hybrid Sampling (SMOTEENN)   
 63  Hybrid Sampling (SMOTEENN)   
 64  Hybrid Sampling (SMOTEENN)   
 65  Hybrid Sampling (SMOTEENN)   
 66  Hybrid Sampling (SMOTEENN)   
 67  Hybrid Sampling (SMOTEENN)   
 68  Hybrid Sampling (SMOTEENN)   
 69  Hybrid Sampling (SMOTEENN)   
 70  Hybrid Sampling (SMOTEENN)   
 71  Hybrid Sampling (SMOTEENN)   
 72  Hybrid Sampling (SMOTEENN)   
 73  Hybrid Sampling (SMOTEENN)   
 74  Hybrid Sampling (SMOTEENN)   
 75  Hybrid Sampling (SMOTEENN)   
 76  Hybrid Sampling (SMOTEENN)   
 77  Hybrid Sampling (SMOTEENN)   
 78  Hybrid Sampling (SMOTEENN)   
 79  Hybrid Sampling (SMOTEENN)   
 80  Hybrid Sampling (SMOTEENN)   
 81  Hybrid Sampling (SMOTEENN)   
 82  Hybrid Sampling (SMOTEENN)   
 83  Hybrid Sampling (SMOTEENN)   
 84  Hybrid Sampling (SMOTEENN)   
 85  Hybrid Sampling (SMOTEENN)   
 86  Hybrid Sampling (SMOTEENN)   
 87  Hybrid Sampling (SMOTEENN)   
 88  Hybrid Sampling (SMOTEENN)   
 89  Hybrid Sampling (SMOTEENN)   
 90  Hybrid Sampling (SMOTEENN)   
 91  Hybrid Sampling (SMOTEENN)   
 92  Hybrid Sampling (SMOTEENN)   
 93  Hybrid Sampling (SMOTEENN)   
 94  Hybrid Sampling (SMOTEENN)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 1   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 2   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 3   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 4   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 5   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 6   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 7   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 8   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 9   Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 10  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 11  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 12  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 13  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 14  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 15  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 16  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 17  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 18  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 19  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 20  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 21  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 22  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 23  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 24  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 25  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 26  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 27  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 28  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 29  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 30  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 31  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 32  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 33  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 34  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 35  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 36  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 37  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 38  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 39  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 40  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 41  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 42  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 43  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 44  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 45  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 46  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 47  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 48  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 49  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 50  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 51  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 52  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 53  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 54  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 55  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 56  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 57  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 58  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 59  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 60  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 61  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 62  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 63  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 64  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 65  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 66  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 67  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 68  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 69  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 70  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 71  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 72  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 73  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 74  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 75  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 76  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 77  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 78  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 79  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 80  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 81  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 82  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 83  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 84  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 85  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 86  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 87  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 88  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 89  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 90  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 91  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 92  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 93  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  
 94  Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.854622             NaN      Zero Imputation   
 1   Decision Trees  0.097187             NaN      Zero Imputation   
 2   Decision Trees  0.160338             NaN      Zero Imputation   
 3   Decision Trees  0.121019             NaN      Zero Imputation   
 4   Decision Trees  0.541203             NaN      Zero Imputation   
 5   Decision Trees  0.084267             NaN      Zero Imputation   
 6   Decision Trees  0.082405             NaN      Zero Imputation   
 7   Decision Trees       NaN             0.0      Zero Imputation   
 8   Decision Trees       NaN            None      Zero Imputation   
 9   Decision Trees       NaN            gini      Zero Imputation   
 10  Decision Trees       NaN            None      Zero Imputation   
 11  Decision Trees       NaN            None      Zero Imputation   
 12  Decision Trees       NaN            None      Zero Imputation   
 13  Decision Trees       NaN             0.0      Zero Imputation   
 14  Decision Trees       NaN               1      Zero Imputation   
 15  Decision Trees       NaN               2      Zero Imputation   
 16  Decision Trees       NaN             0.0      Zero Imputation   
 17  Decision Trees       NaN            None      Zero Imputation   
 18  Decision Trees       NaN            best      Zero Imputation   
 19  Decision Trees  0.861733             NaN      Zero Imputation   
 20  Decision Trees  0.069212             NaN      Zero Imputation   
 21  Decision Trees  0.176829             NaN      Zero Imputation   
 22  Decision Trees  0.099485             NaN      Zero Imputation   
 23  Decision Trees  0.570569             NaN      Zero Imputation   
 24  Decision Trees  0.150801             NaN      Zero Imputation   
 25  Decision Trees  0.141138             NaN      Zero Imputation   
 26  Decision Trees       NaN             0.0      Zero Imputation   
 27  Decision Trees       NaN            None      Zero Imputation   
 28  Decision Trees       NaN            gini      Zero Imputation   
 29  Decision Trees       NaN            None      Zero Imputation   
 30  Decision Trees       NaN            None      Zero Imputation   
 31  Decision Trees       NaN            None      Zero Imputation   
 32  Decision Trees       NaN             0.0      Zero Imputation   
 33  Decision Trees       NaN               1      Zero Imputation   
 34  Decision Trees       NaN               2      Zero Imputation   
 35  Decision Trees       NaN             0.0      Zero Imputation   
 36  Decision Trees       NaN            None      Zero Imputation   
 37  Decision Trees       NaN            best      Zero Imputation   
 38  Decision Trees  0.861470             NaN      Zero Imputation   
 39  Decision Trees  0.081481             NaN      Zero Imputation   
 40  Decision Trees  0.176471             NaN      Zero Imputation   
 41  Decision Trees  0.111486             NaN      Zero Imputation   
 42  Decision Trees  0.537723             NaN      Zero Imputation   
 43  Decision Trees  0.086588             NaN      Zero Imputation   
 44  Decision Trees  0.075446             NaN      Zero Imputation   
 45  Decision Trees       NaN             0.0      Zero Imputation   
 46  Decision Trees       NaN            None      Zero Imputation   
 47  Decision Trees       NaN            gini      Zero Imputation   
 48  Decision Trees       NaN            None      Zero Imputation   
 49  Decision Trees       NaN            None      Zero Imputation   
 50  Decision Trees       NaN            None      Zero Imputation   
 51  Decision Trees       NaN             0.0      Zero Imputation   
 52  Decision Trees       NaN               1      Zero Imputation   
 53  Decision Trees       NaN               2      Zero Imputation   
 54  Decision Trees       NaN             0.0      Zero Imputation   
 55  Decision Trees       NaN            None      Zero Imputation   
 56  Decision Trees       NaN            best      Zero Imputation   
 57  Decision Trees  0.863277             NaN      Zero Imputation   
 58  Decision Trees  0.084833             NaN      Zero Imputation   
 59  Decision Trees  0.168367             NaN      Zero Imputation   
 60  Decision Trees  0.112821             NaN      Zero Imputation   
 61  Decision Trees  0.531852             NaN      Zero Imputation   
 62  Decision Trees  0.070312             NaN      Zero Imputation   
 63  Decision Trees  0.063705             NaN      Zero Imputation   
 64  Decision Trees       NaN             0.0      Zero Imputation   
 65  Decision Trees       NaN            None      Zero Imputation   
 66  Decision Trees       NaN            gini      Zero Imputation   
 67  Decision Trees       NaN            None      Zero Imputation   
 68  Decision Trees       NaN            None      Zero Imputation   
 69  Decision Trees       NaN            None      Zero Imputation   
 70  Decision Trees       NaN             0.0      Zero Imputation   
 71  Decision Trees       NaN               1      Zero Imputation   
 72  Decision Trees       NaN               2      Zero Imputation   
 73  Decision Trees       NaN             0.0      Zero Imputation   
 74  Decision Trees       NaN            None      Zero Imputation   
 75  Decision Trees       NaN            best      Zero Imputation   
 76  Decision Trees  0.865121             NaN      Zero Imputation   
 77  Decision Trees  0.108466             NaN      Zero Imputation   
 78  Decision Trees  0.189815             NaN      Zero Imputation   
 79  Decision Trees  0.138047             NaN      Zero Imputation   
 80  Decision Trees  0.548596             NaN      Zero Imputation   
 81  Decision Trees  0.100631             NaN      Zero Imputation   
 82  Decision Trees  0.097191             NaN      Zero Imputation   
 83  Decision Trees       NaN             0.0      Zero Imputation   
 84  Decision Trees       NaN            None      Zero Imputation   
 85  Decision Trees       NaN            gini      Zero Imputation   
 86  Decision Trees       NaN            None      Zero Imputation   
 87  Decision Trees       NaN            None      Zero Imputation   
 88  Decision Trees       NaN            None      Zero Imputation   
 89  Decision Trees       NaN             0.0      Zero Imputation   
 90  Decision Trees       NaN               1      Zero Imputation   
 91  Decision Trees       NaN               2      Zero Imputation   
 92  Decision Trees       NaN             0.0      Zero Imputation   
 93  Decision Trees       NaN            None      Zero Imputation   
 94  Decision Trees       NaN            best      Zero Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 75  Hybrid Sampling (SMOTETomek)   
 76  Hybrid Sampling (SMOTETomek)   
 77  Hybrid Sampling (SMOTETomek)   
 78  Hybrid Sampling (SMOTETomek)   
 79  Hybrid Sampling (SMOTETomek)   
 80  Hybrid Sampling (SMOTETomek)   
 81  Hybrid Sampling (SMOTETomek)   
 82  Hybrid Sampling (SMOTETomek)   
 83  Hybrid Sampling (SMOTETomek)   
 84  Hybrid Sampling (SMOTETomek)   
 85  Hybrid Sampling (SMOTETomek)   
 86  Hybrid Sampling (SMOTETomek)   
 87  Hybrid Sampling (SMOTETomek)   
 88  Hybrid Sampling (SMOTETomek)   
 89  Hybrid Sampling (SMOTETomek)   
 90  Hybrid Sampling (SMOTETomek)   
 91  Hybrid Sampling (SMOTETomek)   
 92  Hybrid Sampling (SMOTETomek)   
 93  Hybrid Sampling (SMOTETomek)   
 94  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 1   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 2   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 3   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 4   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 5   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 6   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 7   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 8   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 9   Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 10  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 11  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 12  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 13  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 14  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 15  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 16  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 17  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 18  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 19  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 20  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 21  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 22  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 23  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 24  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 25  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 26  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 27  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 28  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 29  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 30  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 31  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 32  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 33  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 34  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 35  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 36  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 37  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 38  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 39  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 40  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 41  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 42  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 43  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 44  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 45  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 46  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 47  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 48  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 49  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 50  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 51  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 52  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 53  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 54  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 55  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 56  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 57  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 58  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 59  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 60  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 61  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 62  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 63  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 64  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 65  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 66  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 67  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 68  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 69  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 70  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 71  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 72  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 73  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 74  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 75  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 76  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 77  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 78  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 79  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 80  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 81  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 82  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 83  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 84  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 85  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 86  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 87  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 88  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 89  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 90  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 91  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 92  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 93  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  
 94  Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.854359             NaN      Mean Imputation   
 1   Decision Trees  0.082011             NaN      Mean Imputation   
 2   Decision Trees  0.130802             NaN      Mean Imputation   
 3   Decision Trees  0.100813             NaN      Mean Imputation   
 4   Decision Trees  0.523625             NaN      Mean Imputation   
 5   Decision Trees  0.049944             NaN      Mean Imputation   
 6   Decision Trees  0.047249             NaN      Mean Imputation   
 7   Decision Trees       NaN             0.0      Mean Imputation   
 8   Decision Trees       NaN            None      Mean Imputation   
 9   Decision Trees       NaN            gini      Mean Imputation   
 10  Decision Trees       NaN            None      Mean Imputation   
 11  Decision Trees       NaN            None      Mean Imputation   
 12  Decision Trees       NaN            None      Mean Imputation   
 13  Decision Trees       NaN             0.0      Mean Imputation   
 14  Decision Trees       NaN               1      Mean Imputation   
 15  Decision Trees       NaN               2      Mean Imputation   
 16  Decision Trees       NaN             0.0      Mean Imputation   
 17  Decision Trees       NaN            None      Mean Imputation   
 18  Decision Trees       NaN            best      Mean Imputation   
 19  Decision Trees  0.858309             NaN      Mean Imputation   
 20  Decision Trees  0.075000             NaN      Mean Imputation   
 21  Decision Trees  0.201220             NaN      Mean Imputation   
 22  Decision Trees  0.109272             NaN      Mean Imputation   
 23  Decision Trees  0.571739             NaN      Mean Imputation   
 24  Decision Trees  0.153045             NaN      Mean Imputation   
 25  Decision Trees  0.143478             NaN      Mean Imputation   
 26  Decision Trees       NaN             0.0      Mean Imputation   
 27  Decision Trees       NaN            None      Mean Imputation   
 28  Decision Trees       NaN            gini      Mean Imputation   
 29  Decision Trees       NaN            None      Mean Imputation   
 30  Decision Trees       NaN            None      Mean Imputation   
 31  Decision Trees       NaN            None      Mean Imputation   
 32  Decision Trees       NaN             0.0      Mean Imputation   
 33  Decision Trees       NaN               1      Mean Imputation   
 34  Decision Trees       NaN               2      Mean Imputation   
 35  Decision Trees       NaN             0.0      Mean Imputation   
 36  Decision Trees       NaN            None      Mean Imputation   
 37  Decision Trees       NaN            best      Mean Imputation   
 38  Decision Trees  0.864367             NaN      Mean Imputation   
 39  Decision Trees  0.094059             NaN      Mean Imputation   
 40  Decision Trees  0.203209             NaN      Mean Imputation   
 41  Decision Trees  0.128596             NaN      Mean Imputation   
 42  Decision Trees  0.552771             NaN      Mean Imputation   
 43  Decision Trees  0.115265             NaN      Mean Imputation   
 44  Decision Trees  0.105542             NaN      Mean Imputation   
 45  Decision Trees       NaN             0.0      Mean Imputation   
 46  Decision Trees       NaN            None      Mean Imputation   
 47  Decision Trees       NaN            gini      Mean Imputation   
 48  Decision Trees       NaN            None      Mean Imputation   
 49  Decision Trees       NaN            None      Mean Imputation   
 50  Decision Trees       NaN            None      Mean Imputation   
 51  Decision Trees       NaN             0.0      Mean Imputation   
 52  Decision Trees       NaN               1      Mean Imputation   
 53  Decision Trees       NaN               2      Mean Imputation   
 54  Decision Trees       NaN             0.0      Mean Imputation   
 55  Decision Trees       NaN            None      Mean Imputation   
 56  Decision Trees       NaN            best      Mean Imputation   
 57  Decision Trees  0.860643             NaN      Mean Imputation   
 58  Decision Trees  0.082707             NaN      Mean Imputation   
 59  Decision Trees  0.168367             NaN      Mean Imputation   
 60  Decision Trees  0.110924             NaN      Mean Imputation   
 61  Decision Trees  0.528842             NaN      Mean Imputation   
 62  Decision Trees  0.068090             NaN      Mean Imputation   
 63  Decision Trees  0.057684             NaN      Mean Imputation   
 64  Decision Trees       NaN             0.0      Mean Imputation   
 65  Decision Trees       NaN            None      Mean Imputation   
 66  Decision Trees       NaN            gini      Mean Imputation   
 67  Decision Trees       NaN            None      Mean Imputation   
 68  Decision Trees       NaN            None      Mean Imputation   
 69  Decision Trees       NaN            None      Mean Imputation   
 70  Decision Trees       NaN             0.0      Mean Imputation   
 71  Decision Trees       NaN               1      Mean Imputation   
 72  Decision Trees       NaN               2      Mean Imputation   
 73  Decision Trees       NaN             0.0      Mean Imputation   
 74  Decision Trees       NaN            None      Mean Imputation   
 75  Decision Trees       NaN            best      Mean Imputation   
 76  Decision Trees  0.858799             NaN      Mean Imputation   
 77  Decision Trees  0.109756             NaN      Mean Imputation   
 78  Decision Trees  0.208333             NaN      Mean Imputation   
 79  Decision Trees  0.143770             NaN      Mean Imputation   
 80  Decision Trees  0.554149             NaN      Mean Imputation   
 81  Decision Trees  0.112166             NaN      Mean Imputation   
 82  Decision Trees  0.108298             NaN      Mean Imputation   
 83  Decision Trees       NaN             0.0      Mean Imputation   
 84  Decision Trees       NaN            None      Mean Imputation   
 85  Decision Trees       NaN            gini      Mean Imputation   
 86  Decision Trees       NaN            None      Mean Imputation   
 87  Decision Trees       NaN            None      Mean Imputation   
 88  Decision Trees       NaN            None      Mean Imputation   
 89  Decision Trees       NaN             0.0      Mean Imputation   
 90  Decision Trees       NaN               1      Mean Imputation   
 91  Decision Trees       NaN               2      Mean Imputation   
 92  Decision Trees       NaN             0.0      Mean Imputation   
 93  Decision Trees       NaN            None      Mean Imputation   
 94  Decision Trees       NaN            best      Mean Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 75  Hybrid Sampling (SMOTETomek)   
 76  Hybrid Sampling (SMOTETomek)   
 77  Hybrid Sampling (SMOTETomek)   
 78  Hybrid Sampling (SMOTETomek)   
 79  Hybrid Sampling (SMOTETomek)   
 80  Hybrid Sampling (SMOTETomek)   
 81  Hybrid Sampling (SMOTETomek)   
 82  Hybrid Sampling (SMOTETomek)   
 83  Hybrid Sampling (SMOTETomek)   
 84  Hybrid Sampling (SMOTETomek)   
 85  Hybrid Sampling (SMOTETomek)   
 86  Hybrid Sampling (SMOTETomek)   
 87  Hybrid Sampling (SMOTETomek)   
 88  Hybrid Sampling (SMOTETomek)   
 89  Hybrid Sampling (SMOTETomek)   
 90  Hybrid Sampling (SMOTETomek)   
 91  Hybrid Sampling (SMOTETomek)   
 92  Hybrid Sampling (SMOTETomek)   
 93  Hybrid Sampling (SMOTETomek)   
 94  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 1   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 2   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 3   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 4   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 5   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 6   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 7   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 8   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 9   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 10  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 11  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 12  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 13  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 14  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 15  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 16  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 17  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 18  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 19  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 20  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 21  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 22  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 23  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 24  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 25  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 26  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 27  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 28  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 29  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 30  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 31  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 32  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 33  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 34  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 35  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 36  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 37  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 38  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 39  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 40  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 41  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 42  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 43  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 44  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 45  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 46  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 47  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 48  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 49  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 50  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 51  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 52  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 53  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 54  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 55  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 56  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 57  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 58  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 59  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 60  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 61  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 62  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 63  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 64  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 65  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 66  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 67  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 68  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 69  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 70  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 71  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 72  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 73  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 74  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 75  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 76  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 77  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 78  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 79  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 80  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 81  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 82  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 83  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 84  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 85  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 86  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 87  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 88  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 89  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 90  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 91  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 92  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 93  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 94  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0   Decision Trees  0.857519             NaN    Median Imputation   
 1   Decision Trees  0.100000             NaN    Median Imputation   
 2   Decision Trees  0.160338             NaN    Median Imputation   
 3   Decision Trees  0.123177             NaN    Median Imputation   
 4   Decision Trees  0.540980             NaN    Median Imputation   
 5   Decision Trees  0.083975             NaN    Median Imputation   
 6   Decision Trees  0.081961             NaN    Median Imputation   
 7   Decision Trees       NaN             0.0    Median Imputation   
 8   Decision Trees       NaN            None    Median Imputation   
 9   Decision Trees       NaN            gini    Median Imputation   
 10  Decision Trees       NaN            None    Median Imputation   
 11  Decision Trees       NaN            None    Median Imputation   
 12  Decision Trees       NaN            None    Median Imputation   
 13  Decision Trees       NaN             0.0    Median Imputation   
 14  Decision Trees       NaN               1    Median Imputation   
 15  Decision Trees       NaN               2    Median Imputation   
 16  Decision Trees       NaN             0.0    Median Imputation   
 17  Decision Trees       NaN            None    Median Imputation   
 18  Decision Trees       NaN            best    Median Imputation   
 19  Decision Trees  0.863577             NaN    Median Imputation   
 20  Decision Trees  0.076555             NaN    Median Imputation   
 21  Decision Trees  0.195122             NaN    Median Imputation   
 22  Decision Trees  0.109966             NaN    Median Imputation   
 23  Decision Trees  0.574620             NaN    Median Imputation   
 24  Decision Trees  0.158825             NaN    Median Imputation   
 25  Decision Trees  0.149240             NaN    Median Imputation   
 26  Decision Trees       NaN             0.0    Median Imputation   
 27  Decision Trees       NaN            None    Median Imputation   
 28  Decision Trees       NaN            gini    Median Imputation   
 29  Decision Trees       NaN            None    Median Imputation   
 30  Decision Trees       NaN            None    Median Imputation   
 31  Decision Trees       NaN            None    Median Imputation   
 32  Decision Trees       NaN             0.0    Median Imputation   
 33  Decision Trees       NaN               1    Median Imputation   
 34  Decision Trees       NaN               2    Median Imputation   
 35  Decision Trees       NaN             0.0    Median Imputation   
 36  Decision Trees       NaN            None    Median Imputation   
 37  Decision Trees       NaN            best    Median Imputation   
 38  Decision Trees  0.862260             NaN    Median Imputation   
 39  Decision Trees  0.098086             NaN    Median Imputation   
 40  Decision Trees  0.219251             NaN    Median Imputation   
 41  Decision Trees  0.135537             NaN    Median Imputation   
 42  Decision Trees  0.566766             NaN    Median Imputation   
 43  Decision Trees  0.144581             NaN    Median Imputation   
 44  Decision Trees  0.133531             NaN    Median Imputation   
 45  Decision Trees       NaN             0.0    Median Imputation   
 46  Decision Trees       NaN            None    Median Imputation   
 47  Decision Trees       NaN            gini    Median Imputation   
 48  Decision Trees       NaN            None    Median Imputation   
 49  Decision Trees       NaN            None    Median Imputation   
 50  Decision Trees       NaN            None    Median Imputation   
 51  Decision Trees       NaN             0.0    Median Imputation   
 52  Decision Trees       NaN               1    Median Imputation   
 53  Decision Trees       NaN               2    Median Imputation   
 54  Decision Trees       NaN             0.0    Median Imputation   
 55  Decision Trees       NaN            None    Median Imputation   
 56  Decision Trees       NaN            best    Median Imputation   
 57  Decision Trees  0.858008             NaN    Median Imputation   
 58  Decision Trees  0.082725             NaN    Median Imputation   
 59  Decision Trees  0.173469             NaN    Median Imputation   
 60  Decision Trees  0.112026             NaN    Median Imputation   
 61  Decision Trees  0.531231             NaN    Median Imputation   
 62  Decision Trees  0.069507             NaN    Median Imputation   
 63  Decision Trees  0.062462             NaN    Median Imputation   
 64  Decision Trees       NaN             0.0    Median Imputation   
 65  Decision Trees       NaN            None    Median Imputation   
 66  Decision Trees       NaN            gini    Median Imputation   
 67  Decision Trees       NaN            None    Median Imputation   
 68  Decision Trees       NaN            None    Median Imputation   
 69  Decision Trees       NaN            None    Median Imputation   
 70  Decision Trees       NaN             0.0    Median Imputation   
 71  Decision Trees       NaN               1    Median Imputation   
 72  Decision Trees       NaN               2    Median Imputation   
 73  Decision Trees       NaN             0.0    Median Imputation   
 74  Decision Trees       NaN            None    Median Imputation   
 75  Decision Trees       NaN            best    Median Imputation   
 76  Decision Trees  0.854847             NaN    Median Imputation   
 77  Decision Trees  0.107728             NaN    Median Imputation   
 78  Decision Trees  0.212963             NaN    Median Imputation   
 79  Decision Trees  0.143079             NaN    Median Imputation   
 80  Decision Trees  0.562375             NaN    Median Imputation   
 81  Decision Trees  0.128890             NaN    Median Imputation   
 82  Decision Trees  0.124749             NaN    Median Imputation   
 83  Decision Trees       NaN             0.0    Median Imputation   
 84  Decision Trees       NaN            None    Median Imputation   
 85  Decision Trees       NaN            gini    Median Imputation   
 86  Decision Trees       NaN            None    Median Imputation   
 87  Decision Trees       NaN            None    Median Imputation   
 88  Decision Trees       NaN            None    Median Imputation   
 89  Decision Trees       NaN             0.0    Median Imputation   
 90  Decision Trees       NaN               1    Median Imputation   
 91  Decision Trees       NaN               2    Median Imputation   
 92  Decision Trees       NaN             0.0    Median Imputation   
 93  Decision Trees       NaN            None    Median Imputation   
 94  Decision Trees       NaN            best    Median Imputation   
 
        Imbalance Class Technique  \
 0   Hybrid Sampling (SMOTETomek)   
 1   Hybrid Sampling (SMOTETomek)   
 2   Hybrid Sampling (SMOTETomek)   
 3   Hybrid Sampling (SMOTETomek)   
 4   Hybrid Sampling (SMOTETomek)   
 5   Hybrid Sampling (SMOTETomek)   
 6   Hybrid Sampling (SMOTETomek)   
 7   Hybrid Sampling (SMOTETomek)   
 8   Hybrid Sampling (SMOTETomek)   
 9   Hybrid Sampling (SMOTETomek)   
 10  Hybrid Sampling (SMOTETomek)   
 11  Hybrid Sampling (SMOTETomek)   
 12  Hybrid Sampling (SMOTETomek)   
 13  Hybrid Sampling (SMOTETomek)   
 14  Hybrid Sampling (SMOTETomek)   
 15  Hybrid Sampling (SMOTETomek)   
 16  Hybrid Sampling (SMOTETomek)   
 17  Hybrid Sampling (SMOTETomek)   
 18  Hybrid Sampling (SMOTETomek)   
 19  Hybrid Sampling (SMOTETomek)   
 20  Hybrid Sampling (SMOTETomek)   
 21  Hybrid Sampling (SMOTETomek)   
 22  Hybrid Sampling (SMOTETomek)   
 23  Hybrid Sampling (SMOTETomek)   
 24  Hybrid Sampling (SMOTETomek)   
 25  Hybrid Sampling (SMOTETomek)   
 26  Hybrid Sampling (SMOTETomek)   
 27  Hybrid Sampling (SMOTETomek)   
 28  Hybrid Sampling (SMOTETomek)   
 29  Hybrid Sampling (SMOTETomek)   
 30  Hybrid Sampling (SMOTETomek)   
 31  Hybrid Sampling (SMOTETomek)   
 32  Hybrid Sampling (SMOTETomek)   
 33  Hybrid Sampling (SMOTETomek)   
 34  Hybrid Sampling (SMOTETomek)   
 35  Hybrid Sampling (SMOTETomek)   
 36  Hybrid Sampling (SMOTETomek)   
 37  Hybrid Sampling (SMOTETomek)   
 38  Hybrid Sampling (SMOTETomek)   
 39  Hybrid Sampling (SMOTETomek)   
 40  Hybrid Sampling (SMOTETomek)   
 41  Hybrid Sampling (SMOTETomek)   
 42  Hybrid Sampling (SMOTETomek)   
 43  Hybrid Sampling (SMOTETomek)   
 44  Hybrid Sampling (SMOTETomek)   
 45  Hybrid Sampling (SMOTETomek)   
 46  Hybrid Sampling (SMOTETomek)   
 47  Hybrid Sampling (SMOTETomek)   
 48  Hybrid Sampling (SMOTETomek)   
 49  Hybrid Sampling (SMOTETomek)   
 50  Hybrid Sampling (SMOTETomek)   
 51  Hybrid Sampling (SMOTETomek)   
 52  Hybrid Sampling (SMOTETomek)   
 53  Hybrid Sampling (SMOTETomek)   
 54  Hybrid Sampling (SMOTETomek)   
 55  Hybrid Sampling (SMOTETomek)   
 56  Hybrid Sampling (SMOTETomek)   
 57  Hybrid Sampling (SMOTETomek)   
 58  Hybrid Sampling (SMOTETomek)   
 59  Hybrid Sampling (SMOTETomek)   
 60  Hybrid Sampling (SMOTETomek)   
 61  Hybrid Sampling (SMOTETomek)   
 62  Hybrid Sampling (SMOTETomek)   
 63  Hybrid Sampling (SMOTETomek)   
 64  Hybrid Sampling (SMOTETomek)   
 65  Hybrid Sampling (SMOTETomek)   
 66  Hybrid Sampling (SMOTETomek)   
 67  Hybrid Sampling (SMOTETomek)   
 68  Hybrid Sampling (SMOTETomek)   
 69  Hybrid Sampling (SMOTETomek)   
 70  Hybrid Sampling (SMOTETomek)   
 71  Hybrid Sampling (SMOTETomek)   
 72  Hybrid Sampling (SMOTETomek)   
 73  Hybrid Sampling (SMOTETomek)   
 74  Hybrid Sampling (SMOTETomek)   
 75  Hybrid Sampling (SMOTETomek)   
 76  Hybrid Sampling (SMOTETomek)   
 77  Hybrid Sampling (SMOTETomek)   
 78  Hybrid Sampling (SMOTETomek)   
 79  Hybrid Sampling (SMOTETomek)   
 80  Hybrid Sampling (SMOTETomek)   
 81  Hybrid Sampling (SMOTETomek)   
 82  Hybrid Sampling (SMOTETomek)   
 83  Hybrid Sampling (SMOTETomek)   
 84  Hybrid Sampling (SMOTETomek)   
 85  Hybrid Sampling (SMOTETomek)   
 86  Hybrid Sampling (SMOTETomek)   
 87  Hybrid Sampling (SMOTETomek)   
 88  Hybrid Sampling (SMOTETomek)   
 89  Hybrid Sampling (SMOTETomek)   
 90  Hybrid Sampling (SMOTETomek)   
 91  Hybrid Sampling (SMOTETomek)   
 92  Hybrid Sampling (SMOTETomek)   
 93  Hybrid Sampling (SMOTETomek)   
 94  Hybrid Sampling (SMOTETomek)   
 
                                     Model Unique Code  
 0   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 1   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 2   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 3   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 4   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 5   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 6   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 7   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 8   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 9   Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 10  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 11  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 12  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 13  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 14  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 15  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 16  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 17  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 18  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 19  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 20  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 21  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 22  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 23  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 24  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 25  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 26  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 27  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 28  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 29  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 30  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 31  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 32  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 33  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 34  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 35  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 36  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 37  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 38  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 39  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 40  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 41  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 42  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 43  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 44  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 45  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 46  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 47  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 48  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 49  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 50  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 51  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 52  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 53  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 54  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 55  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 56  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 57  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 58  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 59  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 60  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 61  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 62  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 63  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 64  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 65  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 66  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 67  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 68  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 69  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 70  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 71  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 72  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 73  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 74  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 75  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 76  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 77  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 78  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 79  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 80  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 81  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 82  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 83  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 84  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 85  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 86  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 87  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 88  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 89  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 90  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 91  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 92  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 93  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  
 94  Decision Trees_Hybrid Sampling (SMOTETomek)_Me...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.903345             NaN      Zero Imputation   
 1    Random Forest  0.134831             NaN      Zero Imputation   
 2    Random Forest  0.101266             NaN      Zero Imputation   
 3    Random Forest  0.115663             NaN      Zero Imputation   
 4    Random Forest  0.659104             NaN      Zero Imputation   
 5    Random Forest  0.284878             NaN      Zero Imputation   
 6    Random Forest  0.318209             NaN      Zero Imputation   
 7    Random Forest       NaN            True      Zero Imputation   
 8    Random Forest       NaN             0.0      Zero Imputation   
 9    Random Forest       NaN            None      Zero Imputation   
 10   Random Forest       NaN            gini      Zero Imputation   
 11   Random Forest       NaN            None      Zero Imputation   
 12   Random Forest       NaN            sqrt      Zero Imputation   
 13   Random Forest       NaN            None      Zero Imputation   
 14   Random Forest       NaN            None      Zero Imputation   
 15   Random Forest       NaN             0.0      Zero Imputation   
 16   Random Forest       NaN               1      Zero Imputation   
 17   Random Forest       NaN               2      Zero Imputation   
 18   Random Forest       NaN             0.0      Zero Imputation   
 19   Random Forest       NaN             100      Zero Imputation   
 20   Random Forest       NaN            None      Zero Imputation   
 21   Random Forest       NaN           False      Zero Imputation   
 22   Random Forest       NaN            None      Zero Imputation   
 23   Random Forest       NaN               0      Zero Imputation   
 24   Random Forest       NaN           False      Zero Imputation   
 25   Random Forest  0.921254             NaN      Zero Imputation   
 26   Random Forest  0.142857             NaN      Zero Imputation   
 27   Random Forest  0.164634             NaN      Zero Imputation   
 28   Random Forest  0.152975             NaN      Zero Imputation   
 29   Random Forest  0.690993             NaN      Zero Imputation   
 30   Random Forest  0.343207             NaN      Zero Imputation   
 31   Random Forest  0.381986             NaN      Zero Imputation   
 32   Random Forest       NaN            True      Zero Imputation   
 33   Random Forest       NaN             0.0      Zero Imputation   
 34   Random Forest       NaN            None      Zero Imputation   
 35   Random Forest       NaN            gini      Zero Imputation   
 36   Random Forest       NaN            None      Zero Imputation   
 37   Random Forest       NaN            sqrt      Zero Imputation   
 38   Random Forest       NaN            None      Zero Imputation   
 39   Random Forest       NaN            None      Zero Imputation   
 40   Random Forest       NaN             0.0      Zero Imputation   
 41   Random Forest       NaN               1      Zero Imputation   
 42   Random Forest       NaN               2      Zero Imputation   
 43   Random Forest       NaN             0.0      Zero Imputation   
 44   Random Forest       NaN             100      Zero Imputation   
 45   Random Forest       NaN            None      Zero Imputation   
 46   Random Forest       NaN           False      Zero Imputation   
 47   Random Forest       NaN            None      Zero Imputation   
 48   Random Forest       NaN               0      Zero Imputation   
 49   Random Forest       NaN           False      Zero Imputation   
 50   Random Forest  0.917830             NaN      Zero Imputation   
 51   Random Forest  0.154696             NaN      Zero Imputation   
 52   Random Forest  0.149733             NaN      Zero Imputation   
 53   Random Forest  0.152174             NaN      Zero Imputation   
 54   Random Forest  0.680292             NaN      Zero Imputation   
 55   Random Forest  0.289813             NaN      Zero Imputation   
 56   Random Forest  0.360585             NaN      Zero Imputation   
 57   Random Forest       NaN            True      Zero Imputation   
 58   Random Forest       NaN             0.0      Zero Imputation   
 59   Random Forest       NaN            None      Zero Imputation   
 60   Random Forest       NaN            gini      Zero Imputation   
 61   Random Forest       NaN            None      Zero Imputation   
 62   Random Forest       NaN            sqrt      Zero Imputation   
 63   Random Forest       NaN            None      Zero Imputation   
 64   Random Forest       NaN            None      Zero Imputation   
 65   Random Forest       NaN             0.0      Zero Imputation   
 66   Random Forest       NaN               1      Zero Imputation   
 67   Random Forest       NaN               2      Zero Imputation   
 68   Random Forest       NaN             0.0      Zero Imputation   
 69   Random Forest       NaN             100      Zero Imputation   
 70   Random Forest       NaN            None      Zero Imputation   
 71   Random Forest       NaN           False      Zero Imputation   
 72   Random Forest       NaN            None      Zero Imputation   
 73   Random Forest       NaN               0      Zero Imputation   
 74   Random Forest       NaN           False      Zero Imputation   
 75   Random Forest  0.911486             NaN      Zero Imputation   
 76   Random Forest  0.119565             NaN      Zero Imputation   
 77   Random Forest  0.112245             NaN      Zero Imputation   
 78   Random Forest  0.115789             NaN      Zero Imputation   
 79   Random Forest  0.619050             NaN      Zero Imputation   
 80   Random Forest  0.215935             NaN      Zero Imputation   
 81   Random Forest  0.238101             NaN      Zero Imputation   
 82   Random Forest       NaN            True      Zero Imputation   
 83   Random Forest       NaN             0.0      Zero Imputation   
 84   Random Forest       NaN            None      Zero Imputation   
 85   Random Forest       NaN            gini      Zero Imputation   
 86   Random Forest       NaN            None      Zero Imputation   
 87   Random Forest       NaN            sqrt      Zero Imputation   
 88   Random Forest       NaN            None      Zero Imputation   
 89   Random Forest       NaN            None      Zero Imputation   
 90   Random Forest       NaN             0.0      Zero Imputation   
 91   Random Forest       NaN               1      Zero Imputation   
 92   Random Forest       NaN               2      Zero Imputation   
 93   Random Forest       NaN             0.0      Zero Imputation   
 94   Random Forest       NaN             100      Zero Imputation   
 95   Random Forest       NaN            None      Zero Imputation   
 96   Random Forest       NaN           False      Zero Imputation   
 97   Random Forest       NaN            None      Zero Imputation   
 98   Random Forest       NaN               0      Zero Imputation   
 99   Random Forest       NaN           False      Zero Imputation   
 100  Random Forest  0.911222             NaN      Zero Imputation   
 101  Random Forest  0.162011             NaN      Zero Imputation   
 102  Random Forest  0.134259             NaN      Zero Imputation   
 103  Random Forest  0.146835             NaN      Zero Imputation   
 104  Random Forest  0.628811             NaN      Zero Imputation   
 105  Random Forest  0.224866             NaN      Zero Imputation   
 106  Random Forest  0.257622             NaN      Zero Imputation   
 107  Random Forest       NaN            True      Zero Imputation   
 108  Random Forest       NaN             0.0      Zero Imputation   
 109  Random Forest       NaN            None      Zero Imputation   
 110  Random Forest       NaN            gini      Zero Imputation   
 111  Random Forest       NaN            None      Zero Imputation   
 112  Random Forest       NaN            sqrt      Zero Imputation   
 113  Random Forest       NaN            None      Zero Imputation   
 114  Random Forest       NaN            None      Zero Imputation   
 115  Random Forest       NaN             0.0      Zero Imputation   
 116  Random Forest       NaN               1      Zero Imputation   
 117  Random Forest       NaN               2      Zero Imputation   
 118  Random Forest       NaN             0.0      Zero Imputation   
 119  Random Forest       NaN             100      Zero Imputation   
 120  Random Forest       NaN            None      Zero Imputation   
 121  Random Forest       NaN           False      Zero Imputation   
 122  Random Forest       NaN            None      Zero Imputation   
 123  Random Forest       NaN               0      Zero Imputation   
 124  Random Forest       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 
                                      Model Unique Code  
 0    Random Forest_Oversampling_Zero Imputation_fold_0  
 1    Random Forest_Oversampling_Zero Imputation_fold_0  
 2    Random Forest_Oversampling_Zero Imputation_fold_0  
 3    Random Forest_Oversampling_Zero Imputation_fold_0  
 4    Random Forest_Oversampling_Zero Imputation_fold_0  
 5    Random Forest_Oversampling_Zero Imputation_fold_0  
 6    Random Forest_Oversampling_Zero Imputation_fold_0  
 7    Random Forest_Oversampling_Zero Imputation_fold_0  
 8    Random Forest_Oversampling_Zero Imputation_fold_0  
 9    Random Forest_Oversampling_Zero Imputation_fold_0  
 10   Random Forest_Oversampling_Zero Imputation_fold_0  
 11   Random Forest_Oversampling_Zero Imputation_fold_0  
 12   Random Forest_Oversampling_Zero Imputation_fold_0  
 13   Random Forest_Oversampling_Zero Imputation_fold_0  
 14   Random Forest_Oversampling_Zero Imputation_fold_0  
 15   Random Forest_Oversampling_Zero Imputation_fold_0  
 16   Random Forest_Oversampling_Zero Imputation_fold_0  
 17   Random Forest_Oversampling_Zero Imputation_fold_0  
 18   Random Forest_Oversampling_Zero Imputation_fold_0  
 19   Random Forest_Oversampling_Zero Imputation_fold_0  
 20   Random Forest_Oversampling_Zero Imputation_fold_0  
 21   Random Forest_Oversampling_Zero Imputation_fold_0  
 22   Random Forest_Oversampling_Zero Imputation_fold_0  
 23   Random Forest_Oversampling_Zero Imputation_fold_0  
 24   Random Forest_Oversampling_Zero Imputation_fold_0  
 25   Random Forest_Oversampling_Zero Imputation_fold_1  
 26   Random Forest_Oversampling_Zero Imputation_fold_1  
 27   Random Forest_Oversampling_Zero Imputation_fold_1  
 28   Random Forest_Oversampling_Zero Imputation_fold_1  
 29   Random Forest_Oversampling_Zero Imputation_fold_1  
 30   Random Forest_Oversampling_Zero Imputation_fold_1  
 31   Random Forest_Oversampling_Zero Imputation_fold_1  
 32   Random Forest_Oversampling_Zero Imputation_fold_1  
 33   Random Forest_Oversampling_Zero Imputation_fold_1  
 34   Random Forest_Oversampling_Zero Imputation_fold_1  
 35   Random Forest_Oversampling_Zero Imputation_fold_1  
 36   Random Forest_Oversampling_Zero Imputation_fold_1  
 37   Random Forest_Oversampling_Zero Imputation_fold_1  
 38   Random Forest_Oversampling_Zero Imputation_fold_1  
 39   Random Forest_Oversampling_Zero Imputation_fold_1  
 40   Random Forest_Oversampling_Zero Imputation_fold_1  
 41   Random Forest_Oversampling_Zero Imputation_fold_1  
 42   Random Forest_Oversampling_Zero Imputation_fold_1  
 43   Random Forest_Oversampling_Zero Imputation_fold_1  
 44   Random Forest_Oversampling_Zero Imputation_fold_1  
 45   Random Forest_Oversampling_Zero Imputation_fold_1  
 46   Random Forest_Oversampling_Zero Imputation_fold_1  
 47   Random Forest_Oversampling_Zero Imputation_fold_1  
 48   Random Forest_Oversampling_Zero Imputation_fold_1  
 49   Random Forest_Oversampling_Zero Imputation_fold_1  
 50   Random Forest_Oversampling_Zero Imputation_fold_2  
 51   Random Forest_Oversampling_Zero Imputation_fold_2  
 52   Random Forest_Oversampling_Zero Imputation_fold_2  
 53   Random Forest_Oversampling_Zero Imputation_fold_2  
 54   Random Forest_Oversampling_Zero Imputation_fold_2  
 55   Random Forest_Oversampling_Zero Imputation_fold_2  
 56   Random Forest_Oversampling_Zero Imputation_fold_2  
 57   Random Forest_Oversampling_Zero Imputation_fold_2  
 58   Random Forest_Oversampling_Zero Imputation_fold_2  
 59   Random Forest_Oversampling_Zero Imputation_fold_2  
 60   Random Forest_Oversampling_Zero Imputation_fold_2  
 61   Random Forest_Oversampling_Zero Imputation_fold_2  
 62   Random Forest_Oversampling_Zero Imputation_fold_2  
 63   Random Forest_Oversampling_Zero Imputation_fold_2  
 64   Random Forest_Oversampling_Zero Imputation_fold_2  
 65   Random Forest_Oversampling_Zero Imputation_fold_2  
 66   Random Forest_Oversampling_Zero Imputation_fold_2  
 67   Random Forest_Oversampling_Zero Imputation_fold_2  
 68   Random Forest_Oversampling_Zero Imputation_fold_2  
 69   Random Forest_Oversampling_Zero Imputation_fold_2  
 70   Random Forest_Oversampling_Zero Imputation_fold_2  
 71   Random Forest_Oversampling_Zero Imputation_fold_2  
 72   Random Forest_Oversampling_Zero Imputation_fold_2  
 73   Random Forest_Oversampling_Zero Imputation_fold_2  
 74   Random Forest_Oversampling_Zero Imputation_fold_2  
 75   Random Forest_Oversampling_Zero Imputation_fold_3  
 76   Random Forest_Oversampling_Zero Imputation_fold_3  
 77   Random Forest_Oversampling_Zero Imputation_fold_3  
 78   Random Forest_Oversampling_Zero Imputation_fold_3  
 79   Random Forest_Oversampling_Zero Imputation_fold_3  
 80   Random Forest_Oversampling_Zero Imputation_fold_3  
 81   Random Forest_Oversampling_Zero Imputation_fold_3  
 82   Random Forest_Oversampling_Zero Imputation_fold_3  
 83   Random Forest_Oversampling_Zero Imputation_fold_3  
 84   Random Forest_Oversampling_Zero Imputation_fold_3  
 85   Random Forest_Oversampling_Zero Imputation_fold_3  
 86   Random Forest_Oversampling_Zero Imputation_fold_3  
 87   Random Forest_Oversampling_Zero Imputation_fold_3  
 88   Random Forest_Oversampling_Zero Imputation_fold_3  
 89   Random Forest_Oversampling_Zero Imputation_fold_3  
 90   Random Forest_Oversampling_Zero Imputation_fold_3  
 91   Random Forest_Oversampling_Zero Imputation_fold_3  
 92   Random Forest_Oversampling_Zero Imputation_fold_3  
 93   Random Forest_Oversampling_Zero Imputation_fold_3  
 94   Random Forest_Oversampling_Zero Imputation_fold_3  
 95   Random Forest_Oversampling_Zero Imputation_fold_3  
 96   Random Forest_Oversampling_Zero Imputation_fold_3  
 97   Random Forest_Oversampling_Zero Imputation_fold_3  
 98   Random Forest_Oversampling_Zero Imputation_fold_3  
 99   Random Forest_Oversampling_Zero Imputation_fold_3  
 100  Random Forest_Oversampling_Zero Imputation_fold_4  
 101  Random Forest_Oversampling_Zero Imputation_fold_4  
 102  Random Forest_Oversampling_Zero Imputation_fold_4  
 103  Random Forest_Oversampling_Zero Imputation_fold_4  
 104  Random Forest_Oversampling_Zero Imputation_fold_4  
 105  Random Forest_Oversampling_Zero Imputation_fold_4  
 106  Random Forest_Oversampling_Zero Imputation_fold_4  
 107  Random Forest_Oversampling_Zero Imputation_fold_4  
 108  Random Forest_Oversampling_Zero Imputation_fold_4  
 109  Random Forest_Oversampling_Zero Imputation_fold_4  
 110  Random Forest_Oversampling_Zero Imputation_fold_4  
 111  Random Forest_Oversampling_Zero Imputation_fold_4  
 112  Random Forest_Oversampling_Zero Imputation_fold_4  
 113  Random Forest_Oversampling_Zero Imputation_fold_4  
 114  Random Forest_Oversampling_Zero Imputation_fold_4  
 115  Random Forest_Oversampling_Zero Imputation_fold_4  
 116  Random Forest_Oversampling_Zero Imputation_fold_4  
 117  Random Forest_Oversampling_Zero Imputation_fold_4  
 118  Random Forest_Oversampling_Zero Imputation_fold_4  
 119  Random Forest_Oversampling_Zero Imputation_fold_4  
 120  Random Forest_Oversampling_Zero Imputation_fold_4  
 121  Random Forest_Oversampling_Zero Imputation_fold_4  
 122  Random Forest_Oversampling_Zero Imputation_fold_4  
 123  Random Forest_Oversampling_Zero Imputation_fold_4  
 124  Random Forest_Oversampling_Zero Imputation_fold_4  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.900448             NaN      Mean Imputation   
 1    Random Forest  0.126984             NaN      Mean Imputation   
 2    Random Forest  0.101266             NaN      Mean Imputation   
 3    Random Forest  0.112676             NaN      Mean Imputation   
 4    Random Forest  0.649686             NaN      Mean Imputation   
 5    Random Forest  0.265185             NaN      Mean Imputation   
 6    Random Forest  0.299372             NaN      Mean Imputation   
 7    Random Forest       NaN            True      Mean Imputation   
 8    Random Forest       NaN             0.0      Mean Imputation   
 9    Random Forest       NaN            None      Mean Imputation   
 10   Random Forest       NaN            gini      Mean Imputation   
 11   Random Forest       NaN            None      Mean Imputation   
 12   Random Forest       NaN            sqrt      Mean Imputation   
 13   Random Forest       NaN            None      Mean Imputation   
 14   Random Forest       NaN            None      Mean Imputation   
 15   Random Forest       NaN             0.0      Mean Imputation   
 16   Random Forest       NaN               1      Mean Imputation   
 17   Random Forest       NaN               2      Mean Imputation   
 18   Random Forest       NaN             0.0      Mean Imputation   
 19   Random Forest       NaN             100      Mean Imputation   
 20   Random Forest       NaN            None      Mean Imputation   
 21   Random Forest       NaN           False      Mean Imputation   
 22   Random Forest       NaN            None      Mean Imputation   
 23   Random Forest       NaN               0      Mean Imputation   
 24   Random Forest       NaN           False      Mean Imputation   
 25   Random Forest  0.921254             NaN      Mean Imputation   
 26   Random Forest  0.146597             NaN      Mean Imputation   
 27   Random Forest  0.170732             NaN      Mean Imputation   
 28   Random Forest  0.157746             NaN      Mean Imputation   
 29   Random Forest  0.666996             NaN      Mean Imputation   
 30   Random Forest  0.299796             NaN      Mean Imputation   
 31   Random Forest  0.333991             NaN      Mean Imputation   
 32   Random Forest       NaN            True      Mean Imputation   
 33   Random Forest       NaN             0.0      Mean Imputation   
 34   Random Forest       NaN            None      Mean Imputation   
 35   Random Forest       NaN            gini      Mean Imputation   
 36   Random Forest       NaN            None      Mean Imputation   
 37   Random Forest       NaN            sqrt      Mean Imputation   
 38   Random Forest       NaN            None      Mean Imputation   
 39   Random Forest       NaN            None      Mean Imputation   
 40   Random Forest       NaN             0.0      Mean Imputation   
 41   Random Forest       NaN               1      Mean Imputation   
 42   Random Forest       NaN               2      Mean Imputation   
 43   Random Forest       NaN             0.0      Mean Imputation   
 44   Random Forest       NaN             100      Mean Imputation   
 45   Random Forest       NaN            None      Mean Imputation   
 46   Random Forest       NaN           False      Mean Imputation   
 47   Random Forest       NaN            None      Mean Imputation   
 48   Random Forest       NaN               0      Mean Imputation   
 49   Random Forest       NaN           False      Mean Imputation   
 50   Random Forest  0.917830             NaN      Mean Imputation   
 51   Random Forest  0.162162             NaN      Mean Imputation   
 52   Random Forest  0.160428             NaN      Mean Imputation   
 53   Random Forest  0.161290             NaN      Mean Imputation   
 54   Random Forest  0.677444             NaN      Mean Imputation   
 55   Random Forest  0.291525             NaN      Mean Imputation   
 56   Random Forest  0.354888             NaN      Mean Imputation   
 57   Random Forest       NaN            True      Mean Imputation   
 58   Random Forest       NaN             0.0      Mean Imputation   
 59   Random Forest       NaN            None      Mean Imputation   
 60   Random Forest       NaN            gini      Mean Imputation   
 61   Random Forest       NaN            None      Mean Imputation   
 62   Random Forest       NaN            sqrt      Mean Imputation   
 63   Random Forest       NaN            None      Mean Imputation   
 64   Random Forest       NaN            None      Mean Imputation   
 65   Random Forest       NaN             0.0      Mean Imputation   
 66   Random Forest       NaN               1      Mean Imputation   
 67   Random Forest       NaN               2      Mean Imputation   
 68   Random Forest       NaN             0.0      Mean Imputation   
 69   Random Forest       NaN             100      Mean Imputation   
 70   Random Forest       NaN            None      Mean Imputation   
 71   Random Forest       NaN           False      Mean Imputation   
 72   Random Forest       NaN            None      Mean Imputation   
 73   Random Forest       NaN               0      Mean Imputation   
 74   Random Forest       NaN           False      Mean Imputation   
 75   Random Forest  0.913066             NaN      Mean Imputation   
 76   Random Forest  0.127778             NaN      Mean Imputation   
 77   Random Forest  0.117347             NaN      Mean Imputation   
 78   Random Forest  0.122340             NaN      Mean Imputation   
 79   Random Forest  0.608358             NaN      Mean Imputation   
 80   Random Forest  0.199410             NaN      Mean Imputation   
 81   Random Forest  0.216716             NaN      Mean Imputation   
 82   Random Forest       NaN            True      Mean Imputation   
 83   Random Forest       NaN             0.0      Mean Imputation   
 84   Random Forest       NaN            None      Mean Imputation   
 85   Random Forest       NaN            gini      Mean Imputation   
 86   Random Forest       NaN            None      Mean Imputation   
 87   Random Forest       NaN            sqrt      Mean Imputation   
 88   Random Forest       NaN            None      Mean Imputation   
 89   Random Forest       NaN            None      Mean Imputation   
 90   Random Forest       NaN             0.0      Mean Imputation   
 91   Random Forest       NaN               1      Mean Imputation   
 92   Random Forest       NaN               2      Mean Imputation   
 93   Random Forest       NaN             0.0      Mean Imputation   
 94   Random Forest       NaN             100      Mean Imputation   
 95   Random Forest       NaN            None      Mean Imputation   
 96   Random Forest       NaN           False      Mean Imputation   
 97   Random Forest       NaN            None      Mean Imputation   
 98   Random Forest       NaN               0      Mean Imputation   
 99   Random Forest       NaN           False      Mean Imputation   
 100  Random Forest  0.909115             NaN      Mean Imputation   
 101  Random Forest  0.151351             NaN      Mean Imputation   
 102  Random Forest  0.129630             NaN      Mean Imputation   
 103  Random Forest  0.139651             NaN      Mean Imputation   
 104  Random Forest  0.623442             NaN      Mean Imputation   
 105  Random Forest  0.258623             NaN      Mean Imputation   
 106  Random Forest  0.246883             NaN      Mean Imputation   
 107  Random Forest       NaN            True      Mean Imputation   
 108  Random Forest       NaN             0.0      Mean Imputation   
 109  Random Forest       NaN            None      Mean Imputation   
 110  Random Forest       NaN            gini      Mean Imputation   
 111  Random Forest       NaN            None      Mean Imputation   
 112  Random Forest       NaN            sqrt      Mean Imputation   
 113  Random Forest       NaN            None      Mean Imputation   
 114  Random Forest       NaN            None      Mean Imputation   
 115  Random Forest       NaN             0.0      Mean Imputation   
 116  Random Forest       NaN               1      Mean Imputation   
 117  Random Forest       NaN               2      Mean Imputation   
 118  Random Forest       NaN             0.0      Mean Imputation   
 119  Random Forest       NaN             100      Mean Imputation   
 120  Random Forest       NaN            None      Mean Imputation   
 121  Random Forest       NaN           False      Mean Imputation   
 122  Random Forest       NaN            None      Mean Imputation   
 123  Random Forest       NaN               0      Mean Imputation   
 124  Random Forest       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 
                                      Model Unique Code  
 0    Random Forest_Oversampling_Mean Imputation_fold_0  
 1    Random Forest_Oversampling_Mean Imputation_fold_0  
 2    Random Forest_Oversampling_Mean Imputation_fold_0  
 3    Random Forest_Oversampling_Mean Imputation_fold_0  
 4    Random Forest_Oversampling_Mean Imputation_fold_0  
 5    Random Forest_Oversampling_Mean Imputation_fold_0  
 6    Random Forest_Oversampling_Mean Imputation_fold_0  
 7    Random Forest_Oversampling_Mean Imputation_fold_0  
 8    Random Forest_Oversampling_Mean Imputation_fold_0  
 9    Random Forest_Oversampling_Mean Imputation_fold_0  
 10   Random Forest_Oversampling_Mean Imputation_fold_0  
 11   Random Forest_Oversampling_Mean Imputation_fold_0  
 12   Random Forest_Oversampling_Mean Imputation_fold_0  
 13   Random Forest_Oversampling_Mean Imputation_fold_0  
 14   Random Forest_Oversampling_Mean Imputation_fold_0  
 15   Random Forest_Oversampling_Mean Imputation_fold_0  
 16   Random Forest_Oversampling_Mean Imputation_fold_0  
 17   Random Forest_Oversampling_Mean Imputation_fold_0  
 18   Random Forest_Oversampling_Mean Imputation_fold_0  
 19   Random Forest_Oversampling_Mean Imputation_fold_0  
 20   Random Forest_Oversampling_Mean Imputation_fold_0  
 21   Random Forest_Oversampling_Mean Imputation_fold_0  
 22   Random Forest_Oversampling_Mean Imputation_fold_0  
 23   Random Forest_Oversampling_Mean Imputation_fold_0  
 24   Random Forest_Oversampling_Mean Imputation_fold_0  
 25   Random Forest_Oversampling_Mean Imputation_fold_1  
 26   Random Forest_Oversampling_Mean Imputation_fold_1  
 27   Random Forest_Oversampling_Mean Imputation_fold_1  
 28   Random Forest_Oversampling_Mean Imputation_fold_1  
 29   Random Forest_Oversampling_Mean Imputation_fold_1  
 30   Random Forest_Oversampling_Mean Imputation_fold_1  
 31   Random Forest_Oversampling_Mean Imputation_fold_1  
 32   Random Forest_Oversampling_Mean Imputation_fold_1  
 33   Random Forest_Oversampling_Mean Imputation_fold_1  
 34   Random Forest_Oversampling_Mean Imputation_fold_1  
 35   Random Forest_Oversampling_Mean Imputation_fold_1  
 36   Random Forest_Oversampling_Mean Imputation_fold_1  
 37   Random Forest_Oversampling_Mean Imputation_fold_1  
 38   Random Forest_Oversampling_Mean Imputation_fold_1  
 39   Random Forest_Oversampling_Mean Imputation_fold_1  
 40   Random Forest_Oversampling_Mean Imputation_fold_1  
 41   Random Forest_Oversampling_Mean Imputation_fold_1  
 42   Random Forest_Oversampling_Mean Imputation_fold_1  
 43   Random Forest_Oversampling_Mean Imputation_fold_1  
 44   Random Forest_Oversampling_Mean Imputation_fold_1  
 45   Random Forest_Oversampling_Mean Imputation_fold_1  
 46   Random Forest_Oversampling_Mean Imputation_fold_1  
 47   Random Forest_Oversampling_Mean Imputation_fold_1  
 48   Random Forest_Oversampling_Mean Imputation_fold_1  
 49   Random Forest_Oversampling_Mean Imputation_fold_1  
 50   Random Forest_Oversampling_Mean Imputation_fold_2  
 51   Random Forest_Oversampling_Mean Imputation_fold_2  
 52   Random Forest_Oversampling_Mean Imputation_fold_2  
 53   Random Forest_Oversampling_Mean Imputation_fold_2  
 54   Random Forest_Oversampling_Mean Imputation_fold_2  
 55   Random Forest_Oversampling_Mean Imputation_fold_2  
 56   Random Forest_Oversampling_Mean Imputation_fold_2  
 57   Random Forest_Oversampling_Mean Imputation_fold_2  
 58   Random Forest_Oversampling_Mean Imputation_fold_2  
 59   Random Forest_Oversampling_Mean Imputation_fold_2  
 60   Random Forest_Oversampling_Mean Imputation_fold_2  
 61   Random Forest_Oversampling_Mean Imputation_fold_2  
 62   Random Forest_Oversampling_Mean Imputation_fold_2  
 63   Random Forest_Oversampling_Mean Imputation_fold_2  
 64   Random Forest_Oversampling_Mean Imputation_fold_2  
 65   Random Forest_Oversampling_Mean Imputation_fold_2  
 66   Random Forest_Oversampling_Mean Imputation_fold_2  
 67   Random Forest_Oversampling_Mean Imputation_fold_2  
 68   Random Forest_Oversampling_Mean Imputation_fold_2  
 69   Random Forest_Oversampling_Mean Imputation_fold_2  
 70   Random Forest_Oversampling_Mean Imputation_fold_2  
 71   Random Forest_Oversampling_Mean Imputation_fold_2  
 72   Random Forest_Oversampling_Mean Imputation_fold_2  
 73   Random Forest_Oversampling_Mean Imputation_fold_2  
 74   Random Forest_Oversampling_Mean Imputation_fold_2  
 75   Random Forest_Oversampling_Mean Imputation_fold_3  
 76   Random Forest_Oversampling_Mean Imputation_fold_3  
 77   Random Forest_Oversampling_Mean Imputation_fold_3  
 78   Random Forest_Oversampling_Mean Imputation_fold_3  
 79   Random Forest_Oversampling_Mean Imputation_fold_3  
 80   Random Forest_Oversampling_Mean Imputation_fold_3  
 81   Random Forest_Oversampling_Mean Imputation_fold_3  
 82   Random Forest_Oversampling_Mean Imputation_fold_3  
 83   Random Forest_Oversampling_Mean Imputation_fold_3  
 84   Random Forest_Oversampling_Mean Imputation_fold_3  
 85   Random Forest_Oversampling_Mean Imputation_fold_3  
 86   Random Forest_Oversampling_Mean Imputation_fold_3  
 87   Random Forest_Oversampling_Mean Imputation_fold_3  
 88   Random Forest_Oversampling_Mean Imputation_fold_3  
 89   Random Forest_Oversampling_Mean Imputation_fold_3  
 90   Random Forest_Oversampling_Mean Imputation_fold_3  
 91   Random Forest_Oversampling_Mean Imputation_fold_3  
 92   Random Forest_Oversampling_Mean Imputation_fold_3  
 93   Random Forest_Oversampling_Mean Imputation_fold_3  
 94   Random Forest_Oversampling_Mean Imputation_fold_3  
 95   Random Forest_Oversampling_Mean Imputation_fold_3  
 96   Random Forest_Oversampling_Mean Imputation_fold_3  
 97   Random Forest_Oversampling_Mean Imputation_fold_3  
 98   Random Forest_Oversampling_Mean Imputation_fold_3  
 99   Random Forest_Oversampling_Mean Imputation_fold_3  
 100  Random Forest_Oversampling_Mean Imputation_fold_4  
 101  Random Forest_Oversampling_Mean Imputation_fold_4  
 102  Random Forest_Oversampling_Mean Imputation_fold_4  
 103  Random Forest_Oversampling_Mean Imputation_fold_4  
 104  Random Forest_Oversampling_Mean Imputation_fold_4  
 105  Random Forest_Oversampling_Mean Imputation_fold_4  
 106  Random Forest_Oversampling_Mean Imputation_fold_4  
 107  Random Forest_Oversampling_Mean Imputation_fold_4  
 108  Random Forest_Oversampling_Mean Imputation_fold_4  
 109  Random Forest_Oversampling_Mean Imputation_fold_4  
 110  Random Forest_Oversampling_Mean Imputation_fold_4  
 111  Random Forest_Oversampling_Mean Imputation_fold_4  
 112  Random Forest_Oversampling_Mean Imputation_fold_4  
 113  Random Forest_Oversampling_Mean Imputation_fold_4  
 114  Random Forest_Oversampling_Mean Imputation_fold_4  
 115  Random Forest_Oversampling_Mean Imputation_fold_4  
 116  Random Forest_Oversampling_Mean Imputation_fold_4  
 117  Random Forest_Oversampling_Mean Imputation_fold_4  
 118  Random Forest_Oversampling_Mean Imputation_fold_4  
 119  Random Forest_Oversampling_Mean Imputation_fold_4  
 120  Random Forest_Oversampling_Mean Imputation_fold_4  
 121  Random Forest_Oversampling_Mean Imputation_fold_4  
 122  Random Forest_Oversampling_Mean Imputation_fold_4  
 123  Random Forest_Oversampling_Mean Imputation_fold_4  
 124  Random Forest_Oversampling_Mean Imputation_fold_4  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.902291             NaN    Median Imputation   
 1    Random Forest  0.119318             NaN    Median Imputation   
 2    Random Forest  0.088608             NaN    Median Imputation   
 3    Random Forest  0.101695             NaN    Median Imputation   
 4    Random Forest  0.658272             NaN    Median Imputation   
 5    Random Forest  0.264224             NaN    Median Imputation   
 6    Random Forest  0.316545             NaN    Median Imputation   
 7    Random Forest       NaN            True    Median Imputation   
 8    Random Forest       NaN             0.0    Median Imputation   
 9    Random Forest       NaN            None    Median Imputation   
 10   Random Forest       NaN            gini    Median Imputation   
 11   Random Forest       NaN            None    Median Imputation   
 12   Random Forest       NaN            sqrt    Median Imputation   
 13   Random Forest       NaN            None    Median Imputation   
 14   Random Forest       NaN            None    Median Imputation   
 15   Random Forest       NaN             0.0    Median Imputation   
 16   Random Forest       NaN               1    Median Imputation   
 17   Random Forest       NaN               2    Median Imputation   
 18   Random Forest       NaN             0.0    Median Imputation   
 19   Random Forest       NaN             100    Median Imputation   
 20   Random Forest       NaN            None    Median Imputation   
 21   Random Forest       NaN           False    Median Imputation   
 22   Random Forest       NaN            None    Median Imputation   
 23   Random Forest       NaN               0    Median Imputation   
 24   Random Forest       NaN           False    Median Imputation   
 25   Random Forest  0.919673             NaN    Median Imputation   
 26   Random Forest  0.134715             NaN    Median Imputation   
 27   Random Forest  0.158537             NaN    Median Imputation   
 28   Random Forest  0.145658             NaN    Median Imputation   
 29   Random Forest  0.689747             NaN    Median Imputation   
 30   Random Forest  0.310340             NaN    Median Imputation   
 31   Random Forest  0.379494             NaN    Median Imputation   
 32   Random Forest       NaN            True    Median Imputation   
 33   Random Forest       NaN             0.0    Median Imputation   
 34   Random Forest       NaN            None    Median Imputation   
 35   Random Forest       NaN            gini    Median Imputation   
 36   Random Forest       NaN            None    Median Imputation   
 37   Random Forest       NaN            sqrt    Median Imputation   
 38   Random Forest       NaN            None    Median Imputation   
 39   Random Forest       NaN            None    Median Imputation   
 40   Random Forest       NaN             0.0    Median Imputation   
 41   Random Forest       NaN               1    Median Imputation   
 42   Random Forest       NaN               2    Median Imputation   
 43   Random Forest       NaN             0.0    Median Imputation   
 44   Random Forest       NaN             100    Median Imputation   
 45   Random Forest       NaN            None    Median Imputation   
 46   Random Forest       NaN           False    Median Imputation   
 47   Random Forest       NaN            None    Median Imputation   
 48   Random Forest       NaN               0    Median Imputation   
 49   Random Forest       NaN           False    Median Imputation   
 50   Random Forest  0.917830             NaN    Median Imputation   
 51   Random Forest  0.154696             NaN    Median Imputation   
 52   Random Forest  0.149733             NaN    Median Imputation   
 53   Random Forest  0.152174             NaN    Median Imputation   
 54   Random Forest  0.664705             NaN    Median Imputation   
 55   Random Forest  0.255440             NaN    Median Imputation   
 56   Random Forest  0.329410             NaN    Median Imputation   
 57   Random Forest       NaN            True    Median Imputation   
 58   Random Forest       NaN             0.0    Median Imputation   
 59   Random Forest       NaN            None    Median Imputation   
 60   Random Forest       NaN            gini    Median Imputation   
 61   Random Forest       NaN            None    Median Imputation   
 62   Random Forest       NaN            sqrt    Median Imputation   
 63   Random Forest       NaN            None    Median Imputation   
 64   Random Forest       NaN            None    Median Imputation   
 65   Random Forest       NaN             0.0    Median Imputation   
 66   Random Forest       NaN               1    Median Imputation   
 67   Random Forest       NaN               2    Median Imputation   
 68   Random Forest       NaN             0.0    Median Imputation   
 69   Random Forest       NaN             100    Median Imputation   
 70   Random Forest       NaN            None    Median Imputation   
 71   Random Forest       NaN           False    Median Imputation   
 72   Random Forest       NaN            None    Median Imputation   
 73   Random Forest       NaN               0    Median Imputation   
 74   Random Forest       NaN           False    Median Imputation   
 75   Random Forest  0.912803             NaN    Median Imputation   
 76   Random Forest  0.122905             NaN    Median Imputation   
 77   Random Forest  0.112245             NaN    Median Imputation   
 78   Random Forest  0.117333             NaN    Median Imputation   
 79   Random Forest  0.623573             NaN    Median Imputation   
 80   Random Forest  0.208968             NaN    Median Imputation   
 81   Random Forest  0.247146             NaN    Median Imputation   
 82   Random Forest       NaN            True    Median Imputation   
 83   Random Forest       NaN             0.0    Median Imputation   
 84   Random Forest       NaN            None    Median Imputation   
 85   Random Forest       NaN            gini    Median Imputation   
 86   Random Forest       NaN            None    Median Imputation   
 87   Random Forest       NaN            sqrt    Median Imputation   
 88   Random Forest       NaN            None    Median Imputation   
 89   Random Forest       NaN            None    Median Imputation   
 90   Random Forest       NaN             0.0    Median Imputation   
 91   Random Forest       NaN               1    Median Imputation   
 92   Random Forest       NaN               2    Median Imputation   
 93   Random Forest       NaN             0.0    Median Imputation   
 94   Random Forest       NaN             100    Median Imputation   
 95   Random Forest       NaN            None    Median Imputation   
 96   Random Forest       NaN           False    Median Imputation   
 97   Random Forest       NaN            None    Median Imputation   
 98   Random Forest       NaN               0    Median Imputation   
 99   Random Forest       NaN           False    Median Imputation   
 100  Random Forest  0.909905             NaN    Median Imputation   
 101  Random Forest  0.157609             NaN    Median Imputation   
 102  Random Forest  0.134259             NaN    Median Imputation   
 103  Random Forest  0.145000             NaN    Median Imputation   
 104  Random Forest  0.649560             NaN    Median Imputation   
 105  Random Forest  0.260428             NaN    Median Imputation   
 106  Random Forest  0.299121             NaN    Median Imputation   
 107  Random Forest       NaN            True    Median Imputation   
 108  Random Forest       NaN             0.0    Median Imputation   
 109  Random Forest       NaN            None    Median Imputation   
 110  Random Forest       NaN            gini    Median Imputation   
 111  Random Forest       NaN            None    Median Imputation   
 112  Random Forest       NaN            sqrt    Median Imputation   
 113  Random Forest       NaN            None    Median Imputation   
 114  Random Forest       NaN            None    Median Imputation   
 115  Random Forest       NaN             0.0    Median Imputation   
 116  Random Forest       NaN               1    Median Imputation   
 117  Random Forest       NaN               2    Median Imputation   
 118  Random Forest       NaN             0.0    Median Imputation   
 119  Random Forest       NaN             100    Median Imputation   
 120  Random Forest       NaN            None    Median Imputation   
 121  Random Forest       NaN           False    Median Imputation   
 122  Random Forest       NaN            None    Median Imputation   
 123  Random Forest       NaN               0    Median Imputation   
 124  Random Forest       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0                Oversampling   
 1                Oversampling   
 2                Oversampling   
 3                Oversampling   
 4                Oversampling   
 5                Oversampling   
 6                Oversampling   
 7                Oversampling   
 8                Oversampling   
 9                Oversampling   
 10               Oversampling   
 11               Oversampling   
 12               Oversampling   
 13               Oversampling   
 14               Oversampling   
 15               Oversampling   
 16               Oversampling   
 17               Oversampling   
 18               Oversampling   
 19               Oversampling   
 20               Oversampling   
 21               Oversampling   
 22               Oversampling   
 23               Oversampling   
 24               Oversampling   
 25               Oversampling   
 26               Oversampling   
 27               Oversampling   
 28               Oversampling   
 29               Oversampling   
 30               Oversampling   
 31               Oversampling   
 32               Oversampling   
 33               Oversampling   
 34               Oversampling   
 35               Oversampling   
 36               Oversampling   
 37               Oversampling   
 38               Oversampling   
 39               Oversampling   
 40               Oversampling   
 41               Oversampling   
 42               Oversampling   
 43               Oversampling   
 44               Oversampling   
 45               Oversampling   
 46               Oversampling   
 47               Oversampling   
 48               Oversampling   
 49               Oversampling   
 50               Oversampling   
 51               Oversampling   
 52               Oversampling   
 53               Oversampling   
 54               Oversampling   
 55               Oversampling   
 56               Oversampling   
 57               Oversampling   
 58               Oversampling   
 59               Oversampling   
 60               Oversampling   
 61               Oversampling   
 62               Oversampling   
 63               Oversampling   
 64               Oversampling   
 65               Oversampling   
 66               Oversampling   
 67               Oversampling   
 68               Oversampling   
 69               Oversampling   
 70               Oversampling   
 71               Oversampling   
 72               Oversampling   
 73               Oversampling   
 74               Oversampling   
 75               Oversampling   
 76               Oversampling   
 77               Oversampling   
 78               Oversampling   
 79               Oversampling   
 80               Oversampling   
 81               Oversampling   
 82               Oversampling   
 83               Oversampling   
 84               Oversampling   
 85               Oversampling   
 86               Oversampling   
 87               Oversampling   
 88               Oversampling   
 89               Oversampling   
 90               Oversampling   
 91               Oversampling   
 92               Oversampling   
 93               Oversampling   
 94               Oversampling   
 95               Oversampling   
 96               Oversampling   
 97               Oversampling   
 98               Oversampling   
 99               Oversampling   
 100              Oversampling   
 101              Oversampling   
 102              Oversampling   
 103              Oversampling   
 104              Oversampling   
 105              Oversampling   
 106              Oversampling   
 107              Oversampling   
 108              Oversampling   
 109              Oversampling   
 110              Oversampling   
 111              Oversampling   
 112              Oversampling   
 113              Oversampling   
 114              Oversampling   
 115              Oversampling   
 116              Oversampling   
 117              Oversampling   
 118              Oversampling   
 119              Oversampling   
 120              Oversampling   
 121              Oversampling   
 122              Oversampling   
 123              Oversampling   
 124              Oversampling   
 
                                      Model Unique Code  
 0    Random Forest_Oversampling_Median Imputation_f...  
 1    Random Forest_Oversampling_Median Imputation_f...  
 2    Random Forest_Oversampling_Median Imputation_f...  
 3    Random Forest_Oversampling_Median Imputation_f...  
 4    Random Forest_Oversampling_Median Imputation_f...  
 5    Random Forest_Oversampling_Median Imputation_f...  
 6    Random Forest_Oversampling_Median Imputation_f...  
 7    Random Forest_Oversampling_Median Imputation_f...  
 8    Random Forest_Oversampling_Median Imputation_f...  
 9    Random Forest_Oversampling_Median Imputation_f...  
 10   Random Forest_Oversampling_Median Imputation_f...  
 11   Random Forest_Oversampling_Median Imputation_f...  
 12   Random Forest_Oversampling_Median Imputation_f...  
 13   Random Forest_Oversampling_Median Imputation_f...  
 14   Random Forest_Oversampling_Median Imputation_f...  
 15   Random Forest_Oversampling_Median Imputation_f...  
 16   Random Forest_Oversampling_Median Imputation_f...  
 17   Random Forest_Oversampling_Median Imputation_f...  
 18   Random Forest_Oversampling_Median Imputation_f...  
 19   Random Forest_Oversampling_Median Imputation_f...  
 20   Random Forest_Oversampling_Median Imputation_f...  
 21   Random Forest_Oversampling_Median Imputation_f...  
 22   Random Forest_Oversampling_Median Imputation_f...  
 23   Random Forest_Oversampling_Median Imputation_f...  
 24   Random Forest_Oversampling_Median Imputation_f...  
 25   Random Forest_Oversampling_Median Imputation_f...  
 26   Random Forest_Oversampling_Median Imputation_f...  
 27   Random Forest_Oversampling_Median Imputation_f...  
 28   Random Forest_Oversampling_Median Imputation_f...  
 29   Random Forest_Oversampling_Median Imputation_f...  
 30   Random Forest_Oversampling_Median Imputation_f...  
 31   Random Forest_Oversampling_Median Imputation_f...  
 32   Random Forest_Oversampling_Median Imputation_f...  
 33   Random Forest_Oversampling_Median Imputation_f...  
 34   Random Forest_Oversampling_Median Imputation_f...  
 35   Random Forest_Oversampling_Median Imputation_f...  
 36   Random Forest_Oversampling_Median Imputation_f...  
 37   Random Forest_Oversampling_Median Imputation_f...  
 38   Random Forest_Oversampling_Median Imputation_f...  
 39   Random Forest_Oversampling_Median Imputation_f...  
 40   Random Forest_Oversampling_Median Imputation_f...  
 41   Random Forest_Oversampling_Median Imputation_f...  
 42   Random Forest_Oversampling_Median Imputation_f...  
 43   Random Forest_Oversampling_Median Imputation_f...  
 44   Random Forest_Oversampling_Median Imputation_f...  
 45   Random Forest_Oversampling_Median Imputation_f...  
 46   Random Forest_Oversampling_Median Imputation_f...  
 47   Random Forest_Oversampling_Median Imputation_f...  
 48   Random Forest_Oversampling_Median Imputation_f...  
 49   Random Forest_Oversampling_Median Imputation_f...  
 50   Random Forest_Oversampling_Median Imputation_f...  
 51   Random Forest_Oversampling_Median Imputation_f...  
 52   Random Forest_Oversampling_Median Imputation_f...  
 53   Random Forest_Oversampling_Median Imputation_f...  
 54   Random Forest_Oversampling_Median Imputation_f...  
 55   Random Forest_Oversampling_Median Imputation_f...  
 56   Random Forest_Oversampling_Median Imputation_f...  
 57   Random Forest_Oversampling_Median Imputation_f...  
 58   Random Forest_Oversampling_Median Imputation_f...  
 59   Random Forest_Oversampling_Median Imputation_f...  
 60   Random Forest_Oversampling_Median Imputation_f...  
 61   Random Forest_Oversampling_Median Imputation_f...  
 62   Random Forest_Oversampling_Median Imputation_f...  
 63   Random Forest_Oversampling_Median Imputation_f...  
 64   Random Forest_Oversampling_Median Imputation_f...  
 65   Random Forest_Oversampling_Median Imputation_f...  
 66   Random Forest_Oversampling_Median Imputation_f...  
 67   Random Forest_Oversampling_Median Imputation_f...  
 68   Random Forest_Oversampling_Median Imputation_f...  
 69   Random Forest_Oversampling_Median Imputation_f...  
 70   Random Forest_Oversampling_Median Imputation_f...  
 71   Random Forest_Oversampling_Median Imputation_f...  
 72   Random Forest_Oversampling_Median Imputation_f...  
 73   Random Forest_Oversampling_Median Imputation_f...  
 74   Random Forest_Oversampling_Median Imputation_f...  
 75   Random Forest_Oversampling_Median Imputation_f...  
 76   Random Forest_Oversampling_Median Imputation_f...  
 77   Random Forest_Oversampling_Median Imputation_f...  
 78   Random Forest_Oversampling_Median Imputation_f...  
 79   Random Forest_Oversampling_Median Imputation_f...  
 80   Random Forest_Oversampling_Median Imputation_f...  
 81   Random Forest_Oversampling_Median Imputation_f...  
 82   Random Forest_Oversampling_Median Imputation_f...  
 83   Random Forest_Oversampling_Median Imputation_f...  
 84   Random Forest_Oversampling_Median Imputation_f...  
 85   Random Forest_Oversampling_Median Imputation_f...  
 86   Random Forest_Oversampling_Median Imputation_f...  
 87   Random Forest_Oversampling_Median Imputation_f...  
 88   Random Forest_Oversampling_Median Imputation_f...  
 89   Random Forest_Oversampling_Median Imputation_f...  
 90   Random Forest_Oversampling_Median Imputation_f...  
 91   Random Forest_Oversampling_Median Imputation_f...  
 92   Random Forest_Oversampling_Median Imputation_f...  
 93   Random Forest_Oversampling_Median Imputation_f...  
 94   Random Forest_Oversampling_Median Imputation_f...  
 95   Random Forest_Oversampling_Median Imputation_f...  
 96   Random Forest_Oversampling_Median Imputation_f...  
 97   Random Forest_Oversampling_Median Imputation_f...  
 98   Random Forest_Oversampling_Median Imputation_f...  
 99   Random Forest_Oversampling_Median Imputation_f...  
 100  Random Forest_Oversampling_Median Imputation_f...  
 101  Random Forest_Oversampling_Median Imputation_f...  
 102  Random Forest_Oversampling_Median Imputation_f...  
 103  Random Forest_Oversampling_Median Imputation_f...  
 104  Random Forest_Oversampling_Median Imputation_f...  
 105  Random Forest_Oversampling_Median Imputation_f...  
 106  Random Forest_Oversampling_Median Imputation_f...  
 107  Random Forest_Oversampling_Median Imputation_f...  
 108  Random Forest_Oversampling_Median Imputation_f...  
 109  Random Forest_Oversampling_Median Imputation_f...  
 110  Random Forest_Oversampling_Median Imputation_f...  
 111  Random Forest_Oversampling_Median Imputation_f...  
 112  Random Forest_Oversampling_Median Imputation_f...  
 113  Random Forest_Oversampling_Median Imputation_f...  
 114  Random Forest_Oversampling_Median Imputation_f...  
 115  Random Forest_Oversampling_Median Imputation_f...  
 116  Random Forest_Oversampling_Median Imputation_f...  
 117  Random Forest_Oversampling_Median Imputation_f...  
 118  Random Forest_Oversampling_Median Imputation_f...  
 119  Random Forest_Oversampling_Median Imputation_f...  
 120  Random Forest_Oversampling_Median Imputation_f...  
 121  Random Forest_Oversampling_Median Imputation_f...  
 122  Random Forest_Oversampling_Median Imputation_f...  
 123  Random Forest_Oversampling_Median Imputation_f...  
 124  Random Forest_Oversampling_Median Imputation_f...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.688965             NaN      Zero Imputation   
 1    Random Forest  0.118740             NaN      Zero Imputation   
 2    Random Forest  0.620253             NaN      Zero Imputation   
 3    Random Forest  0.199322             NaN      Zero Imputation   
 4    Random Forest  0.696303             NaN      Zero Imputation   
 5    Random Forest  0.320868             NaN      Zero Imputation   
 6    Random Forest  0.392605             NaN      Zero Imputation   
 7    Random Forest       NaN            True      Zero Imputation   
 8    Random Forest       NaN             0.0      Zero Imputation   
 9    Random Forest       NaN            None      Zero Imputation   
 10   Random Forest       NaN            gini      Zero Imputation   
 11   Random Forest       NaN            None      Zero Imputation   
 12   Random Forest       NaN            sqrt      Zero Imputation   
 13   Random Forest       NaN            None      Zero Imputation   
 14   Random Forest       NaN            None      Zero Imputation   
 15   Random Forest       NaN             0.0      Zero Imputation   
 16   Random Forest       NaN               1      Zero Imputation   
 17   Random Forest       NaN               2      Zero Imputation   
 18   Random Forest       NaN             0.0      Zero Imputation   
 19   Random Forest       NaN             100      Zero Imputation   
 20   Random Forest       NaN            None      Zero Imputation   
 21   Random Forest       NaN           False      Zero Imputation   
 22   Random Forest       NaN            None      Zero Imputation   
 23   Random Forest       NaN               0      Zero Imputation   
 24   Random Forest       NaN           False      Zero Imputation   
 25   Random Forest  0.675007             NaN      Zero Imputation   
 26   Random Forest  0.078740             NaN      Zero Imputation   
 27   Random Forest  0.609756             NaN      Zero Imputation   
 28   Random Forest  0.139470             NaN      Zero Imputation   
 29   Random Forest  0.688446             NaN      Zero Imputation   
 30   Random Forest  0.290167             NaN      Zero Imputation   
 31   Random Forest  0.376892             NaN      Zero Imputation   
 32   Random Forest       NaN            True      Zero Imputation   
 33   Random Forest       NaN             0.0      Zero Imputation   
 34   Random Forest       NaN            None      Zero Imputation   
 35   Random Forest       NaN            gini      Zero Imputation   
 36   Random Forest       NaN            None      Zero Imputation   
 37   Random Forest       NaN            sqrt      Zero Imputation   
 38   Random Forest       NaN            None      Zero Imputation   
 39   Random Forest       NaN            None      Zero Imputation   
 40   Random Forest       NaN             0.0      Zero Imputation   
 41   Random Forest       NaN               1      Zero Imputation   
 42   Random Forest       NaN               2      Zero Imputation   
 43   Random Forest       NaN             0.0      Zero Imputation   
 44   Random Forest       NaN             100      Zero Imputation   
 45   Random Forest       NaN            None      Zero Imputation   
 46   Random Forest       NaN           False      Zero Imputation   
 47   Random Forest       NaN            None      Zero Imputation   
 48   Random Forest       NaN               0      Zero Imputation   
 49   Random Forest       NaN           False      Zero Imputation   
 50   Random Forest  0.652884             NaN      Zero Imputation   
 51   Random Forest  0.085714             NaN      Zero Imputation   
 52   Random Forest  0.625668             NaN      Zero Imputation   
 53   Random Forest  0.150773             NaN      Zero Imputation   
 54   Random Forest  0.675322             NaN      Zero Imputation   
 55   Random Forest  0.310531             NaN      Zero Imputation   
 56   Random Forest  0.350644             NaN      Zero Imputation   
 57   Random Forest       NaN            True      Zero Imputation   
 58   Random Forest       NaN             0.0      Zero Imputation   
 59   Random Forest       NaN            None      Zero Imputation   
 60   Random Forest       NaN            gini      Zero Imputation   
 61   Random Forest       NaN            None      Zero Imputation   
 62   Random Forest       NaN            sqrt      Zero Imputation   
 63   Random Forest       NaN            None      Zero Imputation   
 64   Random Forest       NaN            None      Zero Imputation   
 65   Random Forest       NaN             0.0      Zero Imputation   
 66   Random Forest       NaN               1      Zero Imputation   
 67   Random Forest       NaN               2      Zero Imputation   
 68   Random Forest       NaN             0.0      Zero Imputation   
 69   Random Forest       NaN             100      Zero Imputation   
 70   Random Forest       NaN            None      Zero Imputation   
 71   Random Forest       NaN           False      Zero Imputation   
 72   Random Forest       NaN            None      Zero Imputation   
 73   Random Forest       NaN               0      Zero Imputation   
 74   Random Forest       NaN           False      Zero Imputation   
 75   Random Forest  0.687829             NaN      Zero Imputation   
 76   Random Forest  0.095004             NaN      Zero Imputation   
 77   Random Forest  0.591837             NaN      Zero Imputation   
 78   Random Forest  0.163726             NaN      Zero Imputation   
 79   Random Forest  0.667464             NaN      Zero Imputation   
 80   Random Forest  0.290023             NaN      Zero Imputation   
 81   Random Forest  0.334928             NaN      Zero Imputation   
 82   Random Forest       NaN            True      Zero Imputation   
 83   Random Forest       NaN             0.0      Zero Imputation   
 84   Random Forest       NaN            None      Zero Imputation   
 85   Random Forest       NaN            gini      Zero Imputation   
 86   Random Forest       NaN            None      Zero Imputation   
 87   Random Forest       NaN            sqrt      Zero Imputation   
 88   Random Forest       NaN            None      Zero Imputation   
 89   Random Forest       NaN            None      Zero Imputation   
 90   Random Forest       NaN             0.0      Zero Imputation   
 91   Random Forest       NaN               1      Zero Imputation   
 92   Random Forest       NaN               2      Zero Imputation   
 93   Random Forest       NaN             0.0      Zero Imputation   
 94   Random Forest       NaN             100      Zero Imputation   
 95   Random Forest       NaN            None      Zero Imputation   
 96   Random Forest       NaN           False      Zero Imputation   
 97   Random Forest       NaN            None      Zero Imputation   
 98   Random Forest       NaN               0      Zero Imputation   
 99   Random Forest       NaN           False      Zero Imputation   
 100  Random Forest  0.678609             NaN      Zero Imputation   
 101  Random Forest  0.100954             NaN      Zero Imputation   
 102  Random Forest  0.587963             NaN      Zero Imputation   
 103  Random Forest  0.172320             NaN      Zero Imputation   
 104  Random Forest  0.683909             NaN      Zero Imputation   
 105  Random Forest  0.299995             NaN      Zero Imputation   
 106  Random Forest  0.367819             NaN      Zero Imputation   
 107  Random Forest       NaN            True      Zero Imputation   
 108  Random Forest       NaN             0.0      Zero Imputation   
 109  Random Forest       NaN            None      Zero Imputation   
 110  Random Forest       NaN            gini      Zero Imputation   
 111  Random Forest       NaN            None      Zero Imputation   
 112  Random Forest       NaN            sqrt      Zero Imputation   
 113  Random Forest       NaN            None      Zero Imputation   
 114  Random Forest       NaN            None      Zero Imputation   
 115  Random Forest       NaN             0.0      Zero Imputation   
 116  Random Forest       NaN               1      Zero Imputation   
 117  Random Forest       NaN               2      Zero Imputation   
 118  Random Forest       NaN             0.0      Zero Imputation   
 119  Random Forest       NaN             100      Zero Imputation   
 120  Random Forest       NaN            None      Zero Imputation   
 121  Random Forest       NaN           False      Zero Imputation   
 122  Random Forest       NaN            None      Zero Imputation   
 123  Random Forest       NaN               0      Zero Imputation   
 124  Random Forest       NaN           False      Zero Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 
                                      Model Unique Code  
 0    Random Forest_Undersampling_Zero Imputation_fo...  
 1    Random Forest_Undersampling_Zero Imputation_fo...  
 2    Random Forest_Undersampling_Zero Imputation_fo...  
 3    Random Forest_Undersampling_Zero Imputation_fo...  
 4    Random Forest_Undersampling_Zero Imputation_fo...  
 5    Random Forest_Undersampling_Zero Imputation_fo...  
 6    Random Forest_Undersampling_Zero Imputation_fo...  
 7    Random Forest_Undersampling_Zero Imputation_fo...  
 8    Random Forest_Undersampling_Zero Imputation_fo...  
 9    Random Forest_Undersampling_Zero Imputation_fo...  
 10   Random Forest_Undersampling_Zero Imputation_fo...  
 11   Random Forest_Undersampling_Zero Imputation_fo...  
 12   Random Forest_Undersampling_Zero Imputation_fo...  
 13   Random Forest_Undersampling_Zero Imputation_fo...  
 14   Random Forest_Undersampling_Zero Imputation_fo...  
 15   Random Forest_Undersampling_Zero Imputation_fo...  
 16   Random Forest_Undersampling_Zero Imputation_fo...  
 17   Random Forest_Undersampling_Zero Imputation_fo...  
 18   Random Forest_Undersampling_Zero Imputation_fo...  
 19   Random Forest_Undersampling_Zero Imputation_fo...  
 20   Random Forest_Undersampling_Zero Imputation_fo...  
 21   Random Forest_Undersampling_Zero Imputation_fo...  
 22   Random Forest_Undersampling_Zero Imputation_fo...  
 23   Random Forest_Undersampling_Zero Imputation_fo...  
 24   Random Forest_Undersampling_Zero Imputation_fo...  
 25   Random Forest_Undersampling_Zero Imputation_fo...  
 26   Random Forest_Undersampling_Zero Imputation_fo...  
 27   Random Forest_Undersampling_Zero Imputation_fo...  
 28   Random Forest_Undersampling_Zero Imputation_fo...  
 29   Random Forest_Undersampling_Zero Imputation_fo...  
 30   Random Forest_Undersampling_Zero Imputation_fo...  
 31   Random Forest_Undersampling_Zero Imputation_fo...  
 32   Random Forest_Undersampling_Zero Imputation_fo...  
 33   Random Forest_Undersampling_Zero Imputation_fo...  
 34   Random Forest_Undersampling_Zero Imputation_fo...  
 35   Random Forest_Undersampling_Zero Imputation_fo...  
 36   Random Forest_Undersampling_Zero Imputation_fo...  
 37   Random Forest_Undersampling_Zero Imputation_fo...  
 38   Random Forest_Undersampling_Zero Imputation_fo...  
 39   Random Forest_Undersampling_Zero Imputation_fo...  
 40   Random Forest_Undersampling_Zero Imputation_fo...  
 41   Random Forest_Undersampling_Zero Imputation_fo...  
 42   Random Forest_Undersampling_Zero Imputation_fo...  
 43   Random Forest_Undersampling_Zero Imputation_fo...  
 44   Random Forest_Undersampling_Zero Imputation_fo...  
 45   Random Forest_Undersampling_Zero Imputation_fo...  
 46   Random Forest_Undersampling_Zero Imputation_fo...  
 47   Random Forest_Undersampling_Zero Imputation_fo...  
 48   Random Forest_Undersampling_Zero Imputation_fo...  
 49   Random Forest_Undersampling_Zero Imputation_fo...  
 50   Random Forest_Undersampling_Zero Imputation_fo...  
 51   Random Forest_Undersampling_Zero Imputation_fo...  
 52   Random Forest_Undersampling_Zero Imputation_fo...  
 53   Random Forest_Undersampling_Zero Imputation_fo...  
 54   Random Forest_Undersampling_Zero Imputation_fo...  
 55   Random Forest_Undersampling_Zero Imputation_fo...  
 56   Random Forest_Undersampling_Zero Imputation_fo...  
 57   Random Forest_Undersampling_Zero Imputation_fo...  
 58   Random Forest_Undersampling_Zero Imputation_fo...  
 59   Random Forest_Undersampling_Zero Imputation_fo...  
 60   Random Forest_Undersampling_Zero Imputation_fo...  
 61   Random Forest_Undersampling_Zero Imputation_fo...  
 62   Random Forest_Undersampling_Zero Imputation_fo...  
 63   Random Forest_Undersampling_Zero Imputation_fo...  
 64   Random Forest_Undersampling_Zero Imputation_fo...  
 65   Random Forest_Undersampling_Zero Imputation_fo...  
 66   Random Forest_Undersampling_Zero Imputation_fo...  
 67   Random Forest_Undersampling_Zero Imputation_fo...  
 68   Random Forest_Undersampling_Zero Imputation_fo...  
 69   Random Forest_Undersampling_Zero Imputation_fo...  
 70   Random Forest_Undersampling_Zero Imputation_fo...  
 71   Random Forest_Undersampling_Zero Imputation_fo...  
 72   Random Forest_Undersampling_Zero Imputation_fo...  
 73   Random Forest_Undersampling_Zero Imputation_fo...  
 74   Random Forest_Undersampling_Zero Imputation_fo...  
 75   Random Forest_Undersampling_Zero Imputation_fo...  
 76   Random Forest_Undersampling_Zero Imputation_fo...  
 77   Random Forest_Undersampling_Zero Imputation_fo...  
 78   Random Forest_Undersampling_Zero Imputation_fo...  
 79   Random Forest_Undersampling_Zero Imputation_fo...  
 80   Random Forest_Undersampling_Zero Imputation_fo...  
 81   Random Forest_Undersampling_Zero Imputation_fo...  
 82   Random Forest_Undersampling_Zero Imputation_fo...  
 83   Random Forest_Undersampling_Zero Imputation_fo...  
 84   Random Forest_Undersampling_Zero Imputation_fo...  
 85   Random Forest_Undersampling_Zero Imputation_fo...  
 86   Random Forest_Undersampling_Zero Imputation_fo...  
 87   Random Forest_Undersampling_Zero Imputation_fo...  
 88   Random Forest_Undersampling_Zero Imputation_fo...  
 89   Random Forest_Undersampling_Zero Imputation_fo...  
 90   Random Forest_Undersampling_Zero Imputation_fo...  
 91   Random Forest_Undersampling_Zero Imputation_fo...  
 92   Random Forest_Undersampling_Zero Imputation_fo...  
 93   Random Forest_Undersampling_Zero Imputation_fo...  
 94   Random Forest_Undersampling_Zero Imputation_fo...  
 95   Random Forest_Undersampling_Zero Imputation_fo...  
 96   Random Forest_Undersampling_Zero Imputation_fo...  
 97   Random Forest_Undersampling_Zero Imputation_fo...  
 98   Random Forest_Undersampling_Zero Imputation_fo...  
 99   Random Forest_Undersampling_Zero Imputation_fo...  
 100  Random Forest_Undersampling_Zero Imputation_fo...  
 101  Random Forest_Undersampling_Zero Imputation_fo...  
 102  Random Forest_Undersampling_Zero Imputation_fo...  
 103  Random Forest_Undersampling_Zero Imputation_fo...  
 104  Random Forest_Undersampling_Zero Imputation_fo...  
 105  Random Forest_Undersampling_Zero Imputation_fo...  
 106  Random Forest_Undersampling_Zero Imputation_fo...  
 107  Random Forest_Undersampling_Zero Imputation_fo...  
 108  Random Forest_Undersampling_Zero Imputation_fo...  
 109  Random Forest_Undersampling_Zero Imputation_fo...  
 110  Random Forest_Undersampling_Zero Imputation_fo...  
 111  Random Forest_Undersampling_Zero Imputation_fo...  
 112  Random Forest_Undersampling_Zero Imputation_fo...  
 113  Random Forest_Undersampling_Zero Imputation_fo...  
 114  Random Forest_Undersampling_Zero Imputation_fo...  
 115  Random Forest_Undersampling_Zero Imputation_fo...  
 116  Random Forest_Undersampling_Zero Imputation_fo...  
 117  Random Forest_Undersampling_Zero Imputation_fo...  
 118  Random Forest_Undersampling_Zero Imputation_fo...  
 119  Random Forest_Undersampling_Zero Imputation_fo...  
 120  Random Forest_Undersampling_Zero Imputation_fo...  
 121  Random Forest_Undersampling_Zero Imputation_fo...  
 122  Random Forest_Undersampling_Zero Imputation_fo...  
 123  Random Forest_Undersampling_Zero Imputation_fo...  
 124  Random Forest_Undersampling_Zero Imputation_fo...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.684224             NaN      Mean Imputation   
 1    Random Forest  0.110211             NaN      Mean Imputation   
 2    Random Forest  0.573840             NaN      Mean Imputation   
 3    Random Forest  0.184908             NaN      Mean Imputation   
 4    Random Forest  0.686369             NaN      Mean Imputation   
 5    Random Forest  0.304038             NaN      Mean Imputation   
 6    Random Forest  0.372737             NaN      Mean Imputation   
 7    Random Forest       NaN            True      Mean Imputation   
 8    Random Forest       NaN             0.0      Mean Imputation   
 9    Random Forest       NaN            None      Mean Imputation   
 10   Random Forest       NaN            gini      Mean Imputation   
 11   Random Forest       NaN            None      Mean Imputation   
 12   Random Forest       NaN            sqrt      Mean Imputation   
 13   Random Forest       NaN            None      Mean Imputation   
 14   Random Forest       NaN            None      Mean Imputation   
 15   Random Forest       NaN             0.0      Mean Imputation   
 16   Random Forest       NaN               1      Mean Imputation   
 17   Random Forest       NaN               2      Mean Imputation   
 18   Random Forest       NaN             0.0      Mean Imputation   
 19   Random Forest       NaN             100      Mean Imputation   
 20   Random Forest       NaN            None      Mean Imputation   
 21   Random Forest       NaN           False      Mean Imputation   
 22   Random Forest       NaN            None      Mean Imputation   
 23   Random Forest       NaN               0      Mean Imputation   
 24   Random Forest       NaN           False      Mean Imputation   
 25   Random Forest  0.693179             NaN      Mean Imputation   
 26   Random Forest  0.077637             NaN      Mean Imputation   
 27   Random Forest  0.560976             NaN      Mean Imputation   
 28   Random Forest  0.136397             NaN      Mean Imputation   
 29   Random Forest  0.699138             NaN      Mean Imputation   
 30   Random Forest  0.302441             NaN      Mean Imputation   
 31   Random Forest  0.398277             NaN      Mean Imputation   
 32   Random Forest       NaN            True      Mean Imputation   
 33   Random Forest       NaN             0.0      Mean Imputation   
 34   Random Forest       NaN            None      Mean Imputation   
 35   Random Forest       NaN            gini      Mean Imputation   
 36   Random Forest       NaN            None      Mean Imputation   
 37   Random Forest       NaN            sqrt      Mean Imputation   
 38   Random Forest       NaN            None      Mean Imputation   
 39   Random Forest       NaN            None      Mean Imputation   
 40   Random Forest       NaN             0.0      Mean Imputation   
 41   Random Forest       NaN               1      Mean Imputation   
 42   Random Forest       NaN               2      Mean Imputation   
 43   Random Forest       NaN             0.0      Mean Imputation   
 44   Random Forest       NaN             100      Mean Imputation   
 45   Random Forest       NaN            None      Mean Imputation   
 46   Random Forest       NaN           False      Mean Imputation   
 47   Random Forest       NaN            None      Mean Imputation   
 48   Random Forest       NaN               0      Mean Imputation   
 49   Random Forest       NaN           False      Mean Imputation   
 50   Random Forest  0.657361             NaN      Mean Imputation   
 51   Random Forest  0.089838             NaN      Mean Imputation   
 52   Random Forest  0.652406             NaN      Mean Imputation   
 53   Random Forest  0.157929             NaN      Mean Imputation   
 54   Random Forest  0.692505             NaN      Mean Imputation   
 55   Random Forest  0.310578             NaN      Mean Imputation   
 56   Random Forest  0.385010             NaN      Mean Imputation   
 57   Random Forest       NaN            True      Mean Imputation   
 58   Random Forest       NaN             0.0      Mean Imputation   
 59   Random Forest       NaN            None      Mean Imputation   
 60   Random Forest       NaN            gini      Mean Imputation   
 61   Random Forest       NaN            None      Mean Imputation   
 62   Random Forest       NaN            sqrt      Mean Imputation   
 63   Random Forest       NaN            None      Mean Imputation   
 64   Random Forest       NaN            None      Mean Imputation   
 65   Random Forest       NaN             0.0      Mean Imputation   
 66   Random Forest       NaN               1      Mean Imputation   
 67   Random Forest       NaN               2      Mean Imputation   
 68   Random Forest       NaN             0.0      Mean Imputation   
 69   Random Forest       NaN             100      Mean Imputation   
 70   Random Forest       NaN            None      Mean Imputation   
 71   Random Forest       NaN           False      Mean Imputation   
 72   Random Forest       NaN            None      Mean Imputation   
 73   Random Forest       NaN               0      Mean Imputation   
 74   Random Forest       NaN           False      Mean Imputation   
 75   Random Forest  0.673077             NaN      Mean Imputation   
 76   Random Forest  0.093385             NaN      Mean Imputation   
 77   Random Forest  0.612245             NaN      Mean Imputation   
 78   Random Forest  0.162053             NaN      Mean Imputation   
 79   Random Forest  0.679978             NaN      Mean Imputation   
 80   Random Forest  0.293645             NaN      Mean Imputation   
 81   Random Forest  0.359956             NaN      Mean Imputation   
 82   Random Forest       NaN            True      Mean Imputation   
 83   Random Forest       NaN             0.0      Mean Imputation   
 84   Random Forest       NaN            None      Mean Imputation   
 85   Random Forest       NaN            gini      Mean Imputation   
 86   Random Forest       NaN            None      Mean Imputation   
 87   Random Forest       NaN            sqrt      Mean Imputation   
 88   Random Forest       NaN            None      Mean Imputation   
 89   Random Forest       NaN            None      Mean Imputation   
 90   Random Forest       NaN             0.0      Mean Imputation   
 91   Random Forest       NaN               1      Mean Imputation   
 92   Random Forest       NaN               2      Mean Imputation   
 93   Random Forest       NaN             0.0      Mean Imputation   
 94   Random Forest       NaN             100      Mean Imputation   
 95   Random Forest       NaN            None      Mean Imputation   
 96   Random Forest       NaN           False      Mean Imputation   
 97   Random Forest       NaN            None      Mean Imputation   
 98   Random Forest       NaN               0      Mean Imputation   
 99   Random Forest       NaN           False      Mean Imputation   
 100  Random Forest  0.689410             NaN      Mean Imputation   
 101  Random Forest  0.106296             NaN      Mean Imputation   
 102  Random Forest  0.601852             NaN      Mean Imputation   
 103  Random Forest  0.180681             NaN      Mean Imputation   
 104  Random Forest  0.684950             NaN      Mean Imputation   
 105  Random Forest  0.307676             NaN      Mean Imputation   
 106  Random Forest  0.369900             NaN      Mean Imputation   
 107  Random Forest       NaN            True      Mean Imputation   
 108  Random Forest       NaN             0.0      Mean Imputation   
 109  Random Forest       NaN            None      Mean Imputation   
 110  Random Forest       NaN            gini      Mean Imputation   
 111  Random Forest       NaN            None      Mean Imputation   
 112  Random Forest       NaN            sqrt      Mean Imputation   
 113  Random Forest       NaN            None      Mean Imputation   
 114  Random Forest       NaN            None      Mean Imputation   
 115  Random Forest       NaN             0.0      Mean Imputation   
 116  Random Forest       NaN               1      Mean Imputation   
 117  Random Forest       NaN               2      Mean Imputation   
 118  Random Forest       NaN             0.0      Mean Imputation   
 119  Random Forest       NaN             100      Mean Imputation   
 120  Random Forest       NaN            None      Mean Imputation   
 121  Random Forest       NaN           False      Mean Imputation   
 122  Random Forest       NaN            None      Mean Imputation   
 123  Random Forest       NaN               0      Mean Imputation   
 124  Random Forest       NaN           False      Mean Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 
                                      Model Unique Code  
 0    Random Forest_Undersampling_Mean Imputation_fo...  
 1    Random Forest_Undersampling_Mean Imputation_fo...  
 2    Random Forest_Undersampling_Mean Imputation_fo...  
 3    Random Forest_Undersampling_Mean Imputation_fo...  
 4    Random Forest_Undersampling_Mean Imputation_fo...  
 5    Random Forest_Undersampling_Mean Imputation_fo...  
 6    Random Forest_Undersampling_Mean Imputation_fo...  
 7    Random Forest_Undersampling_Mean Imputation_fo...  
 8    Random Forest_Undersampling_Mean Imputation_fo...  
 9    Random Forest_Undersampling_Mean Imputation_fo...  
 10   Random Forest_Undersampling_Mean Imputation_fo...  
 11   Random Forest_Undersampling_Mean Imputation_fo...  
 12   Random Forest_Undersampling_Mean Imputation_fo...  
 13   Random Forest_Undersampling_Mean Imputation_fo...  
 14   Random Forest_Undersampling_Mean Imputation_fo...  
 15   Random Forest_Undersampling_Mean Imputation_fo...  
 16   Random Forest_Undersampling_Mean Imputation_fo...  
 17   Random Forest_Undersampling_Mean Imputation_fo...  
 18   Random Forest_Undersampling_Mean Imputation_fo...  
 19   Random Forest_Undersampling_Mean Imputation_fo...  
 20   Random Forest_Undersampling_Mean Imputation_fo...  
 21   Random Forest_Undersampling_Mean Imputation_fo...  
 22   Random Forest_Undersampling_Mean Imputation_fo...  
 23   Random Forest_Undersampling_Mean Imputation_fo...  
 24   Random Forest_Undersampling_Mean Imputation_fo...  
 25   Random Forest_Undersampling_Mean Imputation_fo...  
 26   Random Forest_Undersampling_Mean Imputation_fo...  
 27   Random Forest_Undersampling_Mean Imputation_fo...  
 28   Random Forest_Undersampling_Mean Imputation_fo...  
 29   Random Forest_Undersampling_Mean Imputation_fo...  
 30   Random Forest_Undersampling_Mean Imputation_fo...  
 31   Random Forest_Undersampling_Mean Imputation_fo...  
 32   Random Forest_Undersampling_Mean Imputation_fo...  
 33   Random Forest_Undersampling_Mean Imputation_fo...  
 34   Random Forest_Undersampling_Mean Imputation_fo...  
 35   Random Forest_Undersampling_Mean Imputation_fo...  
 36   Random Forest_Undersampling_Mean Imputation_fo...  
 37   Random Forest_Undersampling_Mean Imputation_fo...  
 38   Random Forest_Undersampling_Mean Imputation_fo...  
 39   Random Forest_Undersampling_Mean Imputation_fo...  
 40   Random Forest_Undersampling_Mean Imputation_fo...  
 41   Random Forest_Undersampling_Mean Imputation_fo...  
 42   Random Forest_Undersampling_Mean Imputation_fo...  
 43   Random Forest_Undersampling_Mean Imputation_fo...  
 44   Random Forest_Undersampling_Mean Imputation_fo...  
 45   Random Forest_Undersampling_Mean Imputation_fo...  
 46   Random Forest_Undersampling_Mean Imputation_fo...  
 47   Random Forest_Undersampling_Mean Imputation_fo...  
 48   Random Forest_Undersampling_Mean Imputation_fo...  
 49   Random Forest_Undersampling_Mean Imputation_fo...  
 50   Random Forest_Undersampling_Mean Imputation_fo...  
 51   Random Forest_Undersampling_Mean Imputation_fo...  
 52   Random Forest_Undersampling_Mean Imputation_fo...  
 53   Random Forest_Undersampling_Mean Imputation_fo...  
 54   Random Forest_Undersampling_Mean Imputation_fo...  
 55   Random Forest_Undersampling_Mean Imputation_fo...  
 56   Random Forest_Undersampling_Mean Imputation_fo...  
 57   Random Forest_Undersampling_Mean Imputation_fo...  
 58   Random Forest_Undersampling_Mean Imputation_fo...  
 59   Random Forest_Undersampling_Mean Imputation_fo...  
 60   Random Forest_Undersampling_Mean Imputation_fo...  
 61   Random Forest_Undersampling_Mean Imputation_fo...  
 62   Random Forest_Undersampling_Mean Imputation_fo...  
 63   Random Forest_Undersampling_Mean Imputation_fo...  
 64   Random Forest_Undersampling_Mean Imputation_fo...  
 65   Random Forest_Undersampling_Mean Imputation_fo...  
 66   Random Forest_Undersampling_Mean Imputation_fo...  
 67   Random Forest_Undersampling_Mean Imputation_fo...  
 68   Random Forest_Undersampling_Mean Imputation_fo...  
 69   Random Forest_Undersampling_Mean Imputation_fo...  
 70   Random Forest_Undersampling_Mean Imputation_fo...  
 71   Random Forest_Undersampling_Mean Imputation_fo...  
 72   Random Forest_Undersampling_Mean Imputation_fo...  
 73   Random Forest_Undersampling_Mean Imputation_fo...  
 74   Random Forest_Undersampling_Mean Imputation_fo...  
 75   Random Forest_Undersampling_Mean Imputation_fo...  
 76   Random Forest_Undersampling_Mean Imputation_fo...  
 77   Random Forest_Undersampling_Mean Imputation_fo...  
 78   Random Forest_Undersampling_Mean Imputation_fo...  
 79   Random Forest_Undersampling_Mean Imputation_fo...  
 80   Random Forest_Undersampling_Mean Imputation_fo...  
 81   Random Forest_Undersampling_Mean Imputation_fo...  
 82   Random Forest_Undersampling_Mean Imputation_fo...  
 83   Random Forest_Undersampling_Mean Imputation_fo...  
 84   Random Forest_Undersampling_Mean Imputation_fo...  
 85   Random Forest_Undersampling_Mean Imputation_fo...  
 86   Random Forest_Undersampling_Mean Imputation_fo...  
 87   Random Forest_Undersampling_Mean Imputation_fo...  
 88   Random Forest_Undersampling_Mean Imputation_fo...  
 89   Random Forest_Undersampling_Mean Imputation_fo...  
 90   Random Forest_Undersampling_Mean Imputation_fo...  
 91   Random Forest_Undersampling_Mean Imputation_fo...  
 92   Random Forest_Undersampling_Mean Imputation_fo...  
 93   Random Forest_Undersampling_Mean Imputation_fo...  
 94   Random Forest_Undersampling_Mean Imputation_fo...  
 95   Random Forest_Undersampling_Mean Imputation_fo...  
 96   Random Forest_Undersampling_Mean Imputation_fo...  
 97   Random Forest_Undersampling_Mean Imputation_fo...  
 98   Random Forest_Undersampling_Mean Imputation_fo...  
 99   Random Forest_Undersampling_Mean Imputation_fo...  
 100  Random Forest_Undersampling_Mean Imputation_fo...  
 101  Random Forest_Undersampling_Mean Imputation_fo...  
 102  Random Forest_Undersampling_Mean Imputation_fo...  
 103  Random Forest_Undersampling_Mean Imputation_fo...  
 104  Random Forest_Undersampling_Mean Imputation_fo...  
 105  Random Forest_Undersampling_Mean Imputation_fo...  
 106  Random Forest_Undersampling_Mean Imputation_fo...  
 107  Random Forest_Undersampling_Mean Imputation_fo...  
 108  Random Forest_Undersampling_Mean Imputation_fo...  
 109  Random Forest_Undersampling_Mean Imputation_fo...  
 110  Random Forest_Undersampling_Mean Imputation_fo...  
 111  Random Forest_Undersampling_Mean Imputation_fo...  
 112  Random Forest_Undersampling_Mean Imputation_fo...  
 113  Random Forest_Undersampling_Mean Imputation_fo...  
 114  Random Forest_Undersampling_Mean Imputation_fo...  
 115  Random Forest_Undersampling_Mean Imputation_fo...  
 116  Random Forest_Undersampling_Mean Imputation_fo...  
 117  Random Forest_Undersampling_Mean Imputation_fo...  
 118  Random Forest_Undersampling_Mean Imputation_fo...  
 119  Random Forest_Undersampling_Mean Imputation_fo...  
 120  Random Forest_Undersampling_Mean Imputation_fo...  
 121  Random Forest_Undersampling_Mean Imputation_fo...  
 122  Random Forest_Undersampling_Mean Imputation_fo...  
 123  Random Forest_Undersampling_Mean Imputation_fo...  
 124  Random Forest_Undersampling_Mean Imputation_fo...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.678167             NaN    Median Imputation   
 1    Random Forest  0.109437             NaN    Median Imputation   
 2    Random Forest  0.582278             NaN    Median Imputation   
 3    Random Forest  0.184246             NaN    Median Imputation   
 4    Random Forest  0.683444             NaN    Median Imputation   
 5    Random Forest  0.287010             NaN    Median Imputation   
 6    Random Forest  0.366887             NaN    Median Imputation   
 7    Random Forest       NaN            True    Median Imputation   
 8    Random Forest       NaN             0.0    Median Imputation   
 9    Random Forest       NaN            None    Median Imputation   
 10   Random Forest       NaN            gini    Median Imputation   
 11   Random Forest       NaN            None    Median Imputation   
 12   Random Forest       NaN            sqrt    Median Imputation   
 13   Random Forest       NaN            None    Median Imputation   
 14   Random Forest       NaN            None    Median Imputation   
 15   Random Forest       NaN             0.0    Median Imputation   
 16   Random Forest       NaN               1    Median Imputation   
 17   Random Forest       NaN               2    Median Imputation   
 18   Random Forest       NaN             0.0    Median Imputation   
 19   Random Forest       NaN             100    Median Imputation   
 20   Random Forest       NaN            None    Median Imputation   
 21   Random Forest       NaN           False    Median Imputation   
 22   Random Forest       NaN            None    Median Imputation   
 23   Random Forest       NaN               0    Median Imputation   
 24   Random Forest       NaN           False    Median Imputation   
 25   Random Forest  0.673953             NaN    Median Imputation   
 26   Random Forest  0.077165             NaN    Median Imputation   
 27   Random Forest  0.597561             NaN    Median Imputation   
 28   Random Forest  0.136681             NaN    Median Imputation   
 29   Random Forest  0.689483             NaN    Median Imputation   
 30   Random Forest  0.296385             NaN    Median Imputation   
 31   Random Forest  0.378967             NaN    Median Imputation   
 32   Random Forest       NaN            True    Median Imputation   
 33   Random Forest       NaN             0.0    Median Imputation   
 34   Random Forest       NaN            None    Median Imputation   
 35   Random Forest       NaN            gini    Median Imputation   
 36   Random Forest       NaN            None    Median Imputation   
 37   Random Forest       NaN            sqrt    Median Imputation   
 38   Random Forest       NaN            None    Median Imputation   
 39   Random Forest       NaN            None    Median Imputation   
 40   Random Forest       NaN             0.0    Median Imputation   
 41   Random Forest       NaN               1    Median Imputation   
 42   Random Forest       NaN               2    Median Imputation   
 43   Random Forest       NaN             0.0    Median Imputation   
 44   Random Forest       NaN             100    Median Imputation   
 45   Random Forest       NaN            None    Median Imputation   
 46   Random Forest       NaN           False    Median Imputation   
 47   Random Forest       NaN            None    Median Imputation   
 48   Random Forest       NaN               0    Median Imputation   
 49   Random Forest       NaN           False    Median Imputation   
 50   Random Forest  0.655254             NaN    Median Imputation   
 51   Random Forest  0.083210             NaN    Median Imputation   
 52   Random Forest  0.598930             NaN    Median Imputation   
 53   Random Forest  0.146119             NaN    Median Imputation   
 54   Random Forest  0.666184             NaN    Median Imputation   
 55   Random Forest  0.276905             NaN    Median Imputation   
 56   Random Forest  0.332368             NaN    Median Imputation   
 57   Random Forest       NaN            True    Median Imputation   
 58   Random Forest       NaN             0.0    Median Imputation   
 59   Random Forest       NaN            None    Median Imputation   
 60   Random Forest       NaN            gini    Median Imputation   
 61   Random Forest       NaN            None    Median Imputation   
 62   Random Forest       NaN            sqrt    Median Imputation   
 63   Random Forest       NaN            None    Median Imputation   
 64   Random Forest       NaN            None    Median Imputation   
 65   Random Forest       NaN             0.0    Median Imputation   
 66   Random Forest       NaN               1    Median Imputation   
 67   Random Forest       NaN               2    Median Imputation   
 68   Random Forest       NaN             0.0    Median Imputation   
 69   Random Forest       NaN             100    Median Imputation   
 70   Random Forest       NaN            None    Median Imputation   
 71   Random Forest       NaN           False    Median Imputation   
 72   Random Forest       NaN            None    Median Imputation   
 73   Random Forest       NaN               0    Median Imputation   
 74   Random Forest       NaN           False    Median Imputation   
 75   Random Forest  0.669652             NaN    Median Imputation   
 76   Random Forest  0.089922             NaN    Median Imputation   
 77   Random Forest  0.591837             NaN    Median Imputation   
 78   Random Forest  0.156124             NaN    Median Imputation   
 79   Random Forest  0.668080             NaN    Median Imputation   
 80   Random Forest  0.274756             NaN    Median Imputation   
 81   Random Forest  0.336159             NaN    Median Imputation   
 82   Random Forest       NaN            True    Median Imputation   
 83   Random Forest       NaN             0.0    Median Imputation   
 84   Random Forest       NaN            None    Median Imputation   
 85   Random Forest       NaN            gini    Median Imputation   
 86   Random Forest       NaN            None    Median Imputation   
 87   Random Forest       NaN            sqrt    Median Imputation   
 88   Random Forest       NaN            None    Median Imputation   
 89   Random Forest       NaN            None    Median Imputation   
 90   Random Forest       NaN             0.0    Median Imputation   
 91   Random Forest       NaN               1    Median Imputation   
 92   Random Forest       NaN               2    Median Imputation   
 93   Random Forest       NaN             0.0    Median Imputation   
 94   Random Forest       NaN             100    Median Imputation   
 95   Random Forest       NaN            None    Median Imputation   
 96   Random Forest       NaN           False    Median Imputation   
 97   Random Forest       NaN            None    Median Imputation   
 98   Random Forest       NaN               0    Median Imputation   
 99   Random Forest       NaN           False    Median Imputation   
 100  Random Forest  0.679136             NaN    Median Imputation   
 101  Random Forest  0.101115             NaN    Median Imputation   
 102  Random Forest  0.587963             NaN    Median Imputation   
 103  Random Forest  0.172554             NaN    Median Imputation   
 104  Random Forest  0.684388             NaN    Median Imputation   
 105  Random Forest  0.308494             NaN    Median Imputation   
 106  Random Forest  0.368776             NaN    Median Imputation   
 107  Random Forest       NaN            True    Median Imputation   
 108  Random Forest       NaN             0.0    Median Imputation   
 109  Random Forest       NaN            None    Median Imputation   
 110  Random Forest       NaN            gini    Median Imputation   
 111  Random Forest       NaN            None    Median Imputation   
 112  Random Forest       NaN            sqrt    Median Imputation   
 113  Random Forest       NaN            None    Median Imputation   
 114  Random Forest       NaN            None    Median Imputation   
 115  Random Forest       NaN             0.0    Median Imputation   
 116  Random Forest       NaN               1    Median Imputation   
 117  Random Forest       NaN               2    Median Imputation   
 118  Random Forest       NaN             0.0    Median Imputation   
 119  Random Forest       NaN             100    Median Imputation   
 120  Random Forest       NaN            None    Median Imputation   
 121  Random Forest       NaN           False    Median Imputation   
 122  Random Forest       NaN            None    Median Imputation   
 123  Random Forest       NaN               0    Median Imputation   
 124  Random Forest       NaN           False    Median Imputation   
 
     Imbalance Class Technique  \
 0               Undersampling   
 1               Undersampling   
 2               Undersampling   
 3               Undersampling   
 4               Undersampling   
 5               Undersampling   
 6               Undersampling   
 7               Undersampling   
 8               Undersampling   
 9               Undersampling   
 10              Undersampling   
 11              Undersampling   
 12              Undersampling   
 13              Undersampling   
 14              Undersampling   
 15              Undersampling   
 16              Undersampling   
 17              Undersampling   
 18              Undersampling   
 19              Undersampling   
 20              Undersampling   
 21              Undersampling   
 22              Undersampling   
 23              Undersampling   
 24              Undersampling   
 25              Undersampling   
 26              Undersampling   
 27              Undersampling   
 28              Undersampling   
 29              Undersampling   
 30              Undersampling   
 31              Undersampling   
 32              Undersampling   
 33              Undersampling   
 34              Undersampling   
 35              Undersampling   
 36              Undersampling   
 37              Undersampling   
 38              Undersampling   
 39              Undersampling   
 40              Undersampling   
 41              Undersampling   
 42              Undersampling   
 43              Undersampling   
 44              Undersampling   
 45              Undersampling   
 46              Undersampling   
 47              Undersampling   
 48              Undersampling   
 49              Undersampling   
 50              Undersampling   
 51              Undersampling   
 52              Undersampling   
 53              Undersampling   
 54              Undersampling   
 55              Undersampling   
 56              Undersampling   
 57              Undersampling   
 58              Undersampling   
 59              Undersampling   
 60              Undersampling   
 61              Undersampling   
 62              Undersampling   
 63              Undersampling   
 64              Undersampling   
 65              Undersampling   
 66              Undersampling   
 67              Undersampling   
 68              Undersampling   
 69              Undersampling   
 70              Undersampling   
 71              Undersampling   
 72              Undersampling   
 73              Undersampling   
 74              Undersampling   
 75              Undersampling   
 76              Undersampling   
 77              Undersampling   
 78              Undersampling   
 79              Undersampling   
 80              Undersampling   
 81              Undersampling   
 82              Undersampling   
 83              Undersampling   
 84              Undersampling   
 85              Undersampling   
 86              Undersampling   
 87              Undersampling   
 88              Undersampling   
 89              Undersampling   
 90              Undersampling   
 91              Undersampling   
 92              Undersampling   
 93              Undersampling   
 94              Undersampling   
 95              Undersampling   
 96              Undersampling   
 97              Undersampling   
 98              Undersampling   
 99              Undersampling   
 100             Undersampling   
 101             Undersampling   
 102             Undersampling   
 103             Undersampling   
 104             Undersampling   
 105             Undersampling   
 106             Undersampling   
 107             Undersampling   
 108             Undersampling   
 109             Undersampling   
 110             Undersampling   
 111             Undersampling   
 112             Undersampling   
 113             Undersampling   
 114             Undersampling   
 115             Undersampling   
 116             Undersampling   
 117             Undersampling   
 118             Undersampling   
 119             Undersampling   
 120             Undersampling   
 121             Undersampling   
 122             Undersampling   
 123             Undersampling   
 124             Undersampling   
 
                                      Model Unique Code  
 0    Random Forest_Undersampling_Median Imputation_...  
 1    Random Forest_Undersampling_Median Imputation_...  
 2    Random Forest_Undersampling_Median Imputation_...  
 3    Random Forest_Undersampling_Median Imputation_...  
 4    Random Forest_Undersampling_Median Imputation_...  
 5    Random Forest_Undersampling_Median Imputation_...  
 6    Random Forest_Undersampling_Median Imputation_...  
 7    Random Forest_Undersampling_Median Imputation_...  
 8    Random Forest_Undersampling_Median Imputation_...  
 9    Random Forest_Undersampling_Median Imputation_...  
 10   Random Forest_Undersampling_Median Imputation_...  
 11   Random Forest_Undersampling_Median Imputation_...  
 12   Random Forest_Undersampling_Median Imputation_...  
 13   Random Forest_Undersampling_Median Imputation_...  
 14   Random Forest_Undersampling_Median Imputation_...  
 15   Random Forest_Undersampling_Median Imputation_...  
 16   Random Forest_Undersampling_Median Imputation_...  
 17   Random Forest_Undersampling_Median Imputation_...  
 18   Random Forest_Undersampling_Median Imputation_...  
 19   Random Forest_Undersampling_Median Imputation_...  
 20   Random Forest_Undersampling_Median Imputation_...  
 21   Random Forest_Undersampling_Median Imputation_...  
 22   Random Forest_Undersampling_Median Imputation_...  
 23   Random Forest_Undersampling_Median Imputation_...  
 24   Random Forest_Undersampling_Median Imputation_...  
 25   Random Forest_Undersampling_Median Imputation_...  
 26   Random Forest_Undersampling_Median Imputation_...  
 27   Random Forest_Undersampling_Median Imputation_...  
 28   Random Forest_Undersampling_Median Imputation_...  
 29   Random Forest_Undersampling_Median Imputation_...  
 30   Random Forest_Undersampling_Median Imputation_...  
 31   Random Forest_Undersampling_Median Imputation_...  
 32   Random Forest_Undersampling_Median Imputation_...  
 33   Random Forest_Undersampling_Median Imputation_...  
 34   Random Forest_Undersampling_Median Imputation_...  
 35   Random Forest_Undersampling_Median Imputation_...  
 36   Random Forest_Undersampling_Median Imputation_...  
 37   Random Forest_Undersampling_Median Imputation_...  
 38   Random Forest_Undersampling_Median Imputation_...  
 39   Random Forest_Undersampling_Median Imputation_...  
 40   Random Forest_Undersampling_Median Imputation_...  
 41   Random Forest_Undersampling_Median Imputation_...  
 42   Random Forest_Undersampling_Median Imputation_...  
 43   Random Forest_Undersampling_Median Imputation_...  
 44   Random Forest_Undersampling_Median Imputation_...  
 45   Random Forest_Undersampling_Median Imputation_...  
 46   Random Forest_Undersampling_Median Imputation_...  
 47   Random Forest_Undersampling_Median Imputation_...  
 48   Random Forest_Undersampling_Median Imputation_...  
 49   Random Forest_Undersampling_Median Imputation_...  
 50   Random Forest_Undersampling_Median Imputation_...  
 51   Random Forest_Undersampling_Median Imputation_...  
 52   Random Forest_Undersampling_Median Imputation_...  
 53   Random Forest_Undersampling_Median Imputation_...  
 54   Random Forest_Undersampling_Median Imputation_...  
 55   Random Forest_Undersampling_Median Imputation_...  
 56   Random Forest_Undersampling_Median Imputation_...  
 57   Random Forest_Undersampling_Median Imputation_...  
 58   Random Forest_Undersampling_Median Imputation_...  
 59   Random Forest_Undersampling_Median Imputation_...  
 60   Random Forest_Undersampling_Median Imputation_...  
 61   Random Forest_Undersampling_Median Imputation_...  
 62   Random Forest_Undersampling_Median Imputation_...  
 63   Random Forest_Undersampling_Median Imputation_...  
 64   Random Forest_Undersampling_Median Imputation_...  
 65   Random Forest_Undersampling_Median Imputation_...  
 66   Random Forest_Undersampling_Median Imputation_...  
 67   Random Forest_Undersampling_Median Imputation_...  
 68   Random Forest_Undersampling_Median Imputation_...  
 69   Random Forest_Undersampling_Median Imputation_...  
 70   Random Forest_Undersampling_Median Imputation_...  
 71   Random Forest_Undersampling_Median Imputation_...  
 72   Random Forest_Undersampling_Median Imputation_...  
 73   Random Forest_Undersampling_Median Imputation_...  
 74   Random Forest_Undersampling_Median Imputation_...  
 75   Random Forest_Undersampling_Median Imputation_...  
 76   Random Forest_Undersampling_Median Imputation_...  
 77   Random Forest_Undersampling_Median Imputation_...  
 78   Random Forest_Undersampling_Median Imputation_...  
 79   Random Forest_Undersampling_Median Imputation_...  
 80   Random Forest_Undersampling_Median Imputation_...  
 81   Random Forest_Undersampling_Median Imputation_...  
 82   Random Forest_Undersampling_Median Imputation_...  
 83   Random Forest_Undersampling_Median Imputation_...  
 84   Random Forest_Undersampling_Median Imputation_...  
 85   Random Forest_Undersampling_Median Imputation_...  
 86   Random Forest_Undersampling_Median Imputation_...  
 87   Random Forest_Undersampling_Median Imputation_...  
 88   Random Forest_Undersampling_Median Imputation_...  
 89   Random Forest_Undersampling_Median Imputation_...  
 90   Random Forest_Undersampling_Median Imputation_...  
 91   Random Forest_Undersampling_Median Imputation_...  
 92   Random Forest_Undersampling_Median Imputation_...  
 93   Random Forest_Undersampling_Median Imputation_...  
 94   Random Forest_Undersampling_Median Imputation_...  
 95   Random Forest_Undersampling_Median Imputation_...  
 96   Random Forest_Undersampling_Median Imputation_...  
 97   Random Forest_Undersampling_Median Imputation_...  
 98   Random Forest_Undersampling_Median Imputation_...  
 99   Random Forest_Undersampling_Median Imputation_...  
 100  Random Forest_Undersampling_Median Imputation_...  
 101  Random Forest_Undersampling_Median Imputation_...  
 102  Random Forest_Undersampling_Median Imputation_...  
 103  Random Forest_Undersampling_Median Imputation_...  
 104  Random Forest_Undersampling_Median Imputation_...  
 105  Random Forest_Undersampling_Median Imputation_...  
 106  Random Forest_Undersampling_Median Imputation_...  
 107  Random Forest_Undersampling_Median Imputation_...  
 108  Random Forest_Undersampling_Median Imputation_...  
 109  Random Forest_Undersampling_Median Imputation_...  
 110  Random Forest_Undersampling_Median Imputation_...  
 111  Random Forest_Undersampling_Median Imputation_...  
 112  Random Forest_Undersampling_Median Imputation_...  
 113  Random Forest_Undersampling_Median Imputation_...  
 114  Random Forest_Undersampling_Median Imputation_...  
 115  Random Forest_Undersampling_Median Imputation_...  
 116  Random Forest_Undersampling_Median Imputation_...  
 117  Random Forest_Undersampling_Median Imputation_...  
 118  Random Forest_Undersampling_Median Imputation_...  
 119  Random Forest_Undersampling_Median Imputation_...  
 120  Random Forest_Undersampling_Median Imputation_...  
 121  Random Forest_Undersampling_Median Imputation_...  
 122  Random Forest_Undersampling_Median Imputation_...  
 123  Random Forest_Undersampling_Median Imputation_...  
 124  Random Forest_Undersampling_Median Imputation_...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.902291             NaN      Zero Imputation   
 1    Random Forest  0.174757             NaN      Zero Imputation   
 2    Random Forest  0.151899             NaN      Zero Imputation   
 3    Random Forest  0.162528             NaN      Zero Imputation   
 4    Random Forest  0.628520             NaN      Zero Imputation   
 5    Random Forest  0.230599             NaN      Zero Imputation   
 6    Random Forest  0.257040             NaN      Zero Imputation   
 7    Random Forest       NaN            True      Zero Imputation   
 8    Random Forest       NaN             0.0      Zero Imputation   
 9    Random Forest       NaN            None      Zero Imputation   
 10   Random Forest       NaN            gini      Zero Imputation   
 11   Random Forest       NaN            None      Zero Imputation   
 12   Random Forest       NaN            sqrt      Zero Imputation   
 13   Random Forest       NaN            None      Zero Imputation   
 14   Random Forest       NaN            None      Zero Imputation   
 15   Random Forest       NaN             0.0      Zero Imputation   
 16   Random Forest       NaN               1      Zero Imputation   
 17   Random Forest       NaN               2      Zero Imputation   
 18   Random Forest       NaN             0.0      Zero Imputation   
 19   Random Forest       NaN             100      Zero Imputation   
 20   Random Forest       NaN            None      Zero Imputation   
 21   Random Forest       NaN           False      Zero Imputation   
 22   Random Forest       NaN            None      Zero Imputation   
 23   Random Forest       NaN               0      Zero Imputation   
 24   Random Forest       NaN           False      Zero Imputation   
 25   Random Forest  0.898341             NaN      Zero Imputation   
 26   Random Forest  0.114583             NaN      Zero Imputation   
 27   Random Forest  0.201220             NaN      Zero Imputation   
 28   Random Forest  0.146018             NaN      Zero Imputation   
 29   Random Forest  0.650230             NaN      Zero Imputation   
 30   Random Forest  0.308082             NaN      Zero Imputation   
 31   Random Forest  0.300461             NaN      Zero Imputation   
 32   Random Forest       NaN            True      Zero Imputation   
 33   Random Forest       NaN             0.0      Zero Imputation   
 34   Random Forest       NaN            None      Zero Imputation   
 35   Random Forest       NaN            gini      Zero Imputation   
 36   Random Forest       NaN            None      Zero Imputation   
 37   Random Forest       NaN            sqrt      Zero Imputation   
 38   Random Forest       NaN            None      Zero Imputation   
 39   Random Forest       NaN            None      Zero Imputation   
 40   Random Forest       NaN             0.0      Zero Imputation   
 41   Random Forest       NaN               1      Zero Imputation   
 42   Random Forest       NaN               2      Zero Imputation   
 43   Random Forest       NaN             0.0      Zero Imputation   
 44   Random Forest       NaN             100      Zero Imputation   
 45   Random Forest       NaN            None      Zero Imputation   
 46   Random Forest       NaN           False      Zero Imputation   
 47   Random Forest       NaN            None      Zero Imputation   
 48   Random Forest       NaN               0      Zero Imputation   
 49   Random Forest       NaN           False      Zero Imputation   
 50   Random Forest  0.897551             NaN      Zero Imputation   
 51   Random Forest  0.120301             NaN      Zero Imputation   
 52   Random Forest  0.171123             NaN      Zero Imputation   
 53   Random Forest  0.141280             NaN      Zero Imputation   
 54   Random Forest  0.597196             NaN      Zero Imputation   
 55   Random Forest  0.235864             NaN      Zero Imputation   
 56   Random Forest  0.194392             NaN      Zero Imputation   
 57   Random Forest       NaN            True      Zero Imputation   
 58   Random Forest       NaN             0.0      Zero Imputation   
 59   Random Forest       NaN            None      Zero Imputation   
 60   Random Forest       NaN            gini      Zero Imputation   
 61   Random Forest       NaN            None      Zero Imputation   
 62   Random Forest       NaN            sqrt      Zero Imputation   
 63   Random Forest       NaN            None      Zero Imputation   
 64   Random Forest       NaN            None      Zero Imputation   
 65   Random Forest       NaN             0.0      Zero Imputation   
 66   Random Forest       NaN               1      Zero Imputation   
 67   Random Forest       NaN               2      Zero Imputation   
 68   Random Forest       NaN             0.0      Zero Imputation   
 69   Random Forest       NaN             100      Zero Imputation   
 70   Random Forest       NaN            None      Zero Imputation   
 71   Random Forest       NaN           False      Zero Imputation   
 72   Random Forest       NaN            None      Zero Imputation   
 73   Random Forest       NaN               0      Zero Imputation   
 74   Random Forest       NaN           False      Zero Imputation   
 75   Random Forest  0.899368             NaN      Zero Imputation   
 76   Random Forest  0.118852             NaN      Zero Imputation   
 77   Random Forest  0.147959             NaN      Zero Imputation   
 78   Random Forest  0.131818             NaN      Zero Imputation   
 79   Random Forest  0.582183             NaN      Zero Imputation   
 80   Random Forest  0.166400             NaN      Zero Imputation   
 81   Random Forest  0.164365             NaN      Zero Imputation   
 82   Random Forest       NaN            True      Zero Imputation   
 83   Random Forest       NaN             0.0      Zero Imputation   
 84   Random Forest       NaN            None      Zero Imputation   
 85   Random Forest       NaN            gini      Zero Imputation   
 86   Random Forest       NaN            None      Zero Imputation   
 87   Random Forest       NaN            sqrt      Zero Imputation   
 88   Random Forest       NaN            None      Zero Imputation   
 89   Random Forest       NaN            None      Zero Imputation   
 90   Random Forest       NaN             0.0      Zero Imputation   
 91   Random Forest       NaN               1      Zero Imputation   
 92   Random Forest       NaN               2      Zero Imputation   
 93   Random Forest       NaN             0.0      Zero Imputation   
 94   Random Forest       NaN             100      Zero Imputation   
 95   Random Forest       NaN            None      Zero Imputation   
 96   Random Forest       NaN           False      Zero Imputation   
 97   Random Forest       NaN            None      Zero Imputation   
 98   Random Forest       NaN               0      Zero Imputation   
 99   Random Forest       NaN           False      Zero Imputation   
 100  Random Forest  0.894626             NaN      Zero Imputation   
 101  Random Forest  0.151515             NaN      Zero Imputation   
 102  Random Forest  0.185185             NaN      Zero Imputation   
 103  Random Forest  0.166667             NaN      Zero Imputation   
 104  Random Forest  0.601638             NaN      Zero Imputation   
 105  Random Forest  0.242060             NaN      Zero Imputation   
 106  Random Forest  0.203276             NaN      Zero Imputation   
 107  Random Forest       NaN            True      Zero Imputation   
 108  Random Forest       NaN             0.0      Zero Imputation   
 109  Random Forest       NaN            None      Zero Imputation   
 110  Random Forest       NaN            gini      Zero Imputation   
 111  Random Forest       NaN            None      Zero Imputation   
 112  Random Forest       NaN            sqrt      Zero Imputation   
 113  Random Forest       NaN            None      Zero Imputation   
 114  Random Forest       NaN            None      Zero Imputation   
 115  Random Forest       NaN             0.0      Zero Imputation   
 116  Random Forest       NaN               1      Zero Imputation   
 117  Random Forest       NaN               2      Zero Imputation   
 118  Random Forest       NaN             0.0      Zero Imputation   
 119  Random Forest       NaN             100      Zero Imputation   
 120  Random Forest       NaN            None      Zero Imputation   
 121  Random Forest       NaN           False      Zero Imputation   
 122  Random Forest       NaN            None      Zero Imputation   
 123  Random Forest       NaN               0      Zero Imputation   
 124  Random Forest       NaN           False      Zero Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 1    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 2    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 3    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 4    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 5    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 6    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 7    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 8    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 9    Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 10   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 11   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 12   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 13   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 14   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 15   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 16   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 17   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 18   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 19   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 20   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 21   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 22   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 23   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 24   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 25   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 26   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 27   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 28   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 29   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 30   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 31   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 32   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 33   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 34   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 35   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 36   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 37   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 38   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 39   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 40   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 41   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 42   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 43   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 44   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 45   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 46   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 47   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 48   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 49   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 50   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 51   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 52   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 53   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 54   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 55   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 56   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 57   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 58   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 59   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 60   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 61   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 62   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 63   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 64   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 65   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 66   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 67   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 68   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 69   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 70   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 71   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 72   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 73   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 74   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 75   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 76   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 77   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 78   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 79   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 80   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 81   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 82   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 83   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 84   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 85   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 86   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 87   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 88   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 89   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 90   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 91   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 92   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 93   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 94   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 95   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 96   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 97   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 98   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 99   Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 100  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 101  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 102  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 103  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 104  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 105  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 106  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 107  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 108  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 109  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 110  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 111  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 112  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 113  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 114  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 115  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 116  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 117  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 118  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 119  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 120  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 121  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 122  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 123  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  
 124  Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.893337             NaN      Mean Imputation   
 1    Random Forest  0.144068             NaN      Mean Imputation   
 2    Random Forest  0.143460             NaN      Mean Imputation   
 3    Random Forest  0.143763             NaN      Mean Imputation   
 4    Random Forest  0.626762             NaN      Mean Imputation   
 5    Random Forest  0.238124             NaN      Mean Imputation   
 6    Random Forest  0.253524             NaN      Mean Imputation   
 7    Random Forest       NaN            True      Mean Imputation   
 8    Random Forest       NaN             0.0      Mean Imputation   
 9    Random Forest       NaN            None      Mean Imputation   
 10   Random Forest       NaN            gini      Mean Imputation   
 11   Random Forest       NaN            None      Mean Imputation   
 12   Random Forest       NaN            sqrt      Mean Imputation   
 13   Random Forest       NaN            None      Mean Imputation   
 14   Random Forest       NaN            None      Mean Imputation   
 15   Random Forest       NaN             0.0      Mean Imputation   
 16   Random Forest       NaN               1      Mean Imputation   
 17   Random Forest       NaN               2      Mean Imputation   
 18   Random Forest       NaN             0.0      Mean Imputation   
 19   Random Forest       NaN             100      Mean Imputation   
 20   Random Forest       NaN            None      Mean Imputation   
 21   Random Forest       NaN           False      Mean Imputation   
 22   Random Forest       NaN            None      Mean Imputation   
 23   Random Forest       NaN               0      Mean Imputation   
 24   Random Forest       NaN           False      Mean Imputation   
 25   Random Forest  0.905452             NaN      Mean Imputation   
 26   Random Forest  0.132075             NaN      Mean Imputation   
 27   Random Forest  0.213415             NaN      Mean Imputation   
 28   Random Forest  0.163170             NaN      Mean Imputation   
 29   Random Forest  0.640181             NaN      Mean Imputation   
 30   Random Forest  0.261583             NaN      Mean Imputation   
 31   Random Forest  0.280362             NaN      Mean Imputation   
 32   Random Forest       NaN            True      Mean Imputation   
 33   Random Forest       NaN             0.0      Mean Imputation   
 34   Random Forest       NaN            None      Mean Imputation   
 35   Random Forest       NaN            gini      Mean Imputation   
 36   Random Forest       NaN            None      Mean Imputation   
 37   Random Forest       NaN            sqrt      Mean Imputation   
 38   Random Forest       NaN            None      Mean Imputation   
 39   Random Forest       NaN            None      Mean Imputation   
 40   Random Forest       NaN             0.0      Mean Imputation   
 41   Random Forest       NaN               1      Mean Imputation   
 42   Random Forest       NaN               2      Mean Imputation   
 43   Random Forest       NaN             0.0      Mean Imputation   
 44   Random Forest       NaN             100      Mean Imputation   
 45   Random Forest       NaN            None      Mean Imputation   
 46   Random Forest       NaN           False      Mean Imputation   
 47   Random Forest       NaN            None      Mean Imputation   
 48   Random Forest       NaN               0      Mean Imputation   
 49   Random Forest       NaN           False      Mean Imputation   
 50   Random Forest  0.899131             NaN      Mean Imputation   
 51   Random Forest  0.108000             NaN      Mean Imputation   
 52   Random Forest  0.144385             NaN      Mean Imputation   
 53   Random Forest  0.123570             NaN      Mean Imputation   
 54   Random Forest  0.604791             NaN      Mean Imputation   
 55   Random Forest  0.259521             NaN      Mean Imputation   
 56   Random Forest  0.209581             NaN      Mean Imputation   
 57   Random Forest       NaN            True      Mean Imputation   
 58   Random Forest       NaN             0.0      Mean Imputation   
 59   Random Forest       NaN            None      Mean Imputation   
 60   Random Forest       NaN            gini      Mean Imputation   
 61   Random Forest       NaN            None      Mean Imputation   
 62   Random Forest       NaN            sqrt      Mean Imputation   
 63   Random Forest       NaN            None      Mean Imputation   
 64   Random Forest       NaN            None      Mean Imputation   
 65   Random Forest       NaN             0.0      Mean Imputation   
 66   Random Forest       NaN               1      Mean Imputation   
 67   Random Forest       NaN               2      Mean Imputation   
 68   Random Forest       NaN             0.0      Mean Imputation   
 69   Random Forest       NaN             100      Mean Imputation   
 70   Random Forest       NaN            None      Mean Imputation   
 71   Random Forest       NaN           False      Mean Imputation   
 72   Random Forest       NaN            None      Mean Imputation   
 73   Random Forest       NaN               0      Mean Imputation   
 74   Random Forest       NaN           False      Mean Imputation   
 75   Random Forest  0.904636             NaN      Mean Imputation   
 76   Random Forest  0.135965             NaN      Mean Imputation   
 77   Random Forest  0.158163             NaN      Mean Imputation   
 78   Random Forest  0.146226             NaN      Mean Imputation   
 79   Random Forest  0.616870             NaN      Mean Imputation   
 80   Random Forest  0.228418             NaN      Mean Imputation   
 81   Random Forest  0.233740             NaN      Mean Imputation   
 82   Random Forest       NaN            True      Mean Imputation   
 83   Random Forest       NaN             0.0      Mean Imputation   
 84   Random Forest       NaN            None      Mean Imputation   
 85   Random Forest       NaN            gini      Mean Imputation   
 86   Random Forest       NaN            None      Mean Imputation   
 87   Random Forest       NaN            sqrt      Mean Imputation   
 88   Random Forest       NaN            None      Mean Imputation   
 89   Random Forest       NaN            None      Mean Imputation   
 90   Random Forest       NaN             0.0      Mean Imputation   
 91   Random Forest       NaN               1      Mean Imputation   
 92   Random Forest       NaN               2      Mean Imputation   
 93   Random Forest       NaN             0.0      Mean Imputation   
 94   Random Forest       NaN             100      Mean Imputation   
 95   Random Forest       NaN            None      Mean Imputation   
 96   Random Forest       NaN           False      Mean Imputation   
 97   Random Forest       NaN            None      Mean Imputation   
 98   Random Forest       NaN               0      Mean Imputation   
 99   Random Forest       NaN           False      Mean Imputation   
 100  Random Forest  0.897524             NaN      Mean Imputation   
 101  Random Forest  0.141079             NaN      Mean Imputation   
 102  Random Forest  0.157407             NaN      Mean Imputation   
 103  Random Forest  0.148796             NaN      Mean Imputation   
 104  Random Forest  0.585898             NaN      Mean Imputation   
 105  Random Forest  0.207609             NaN      Mean Imputation   
 106  Random Forest  0.171797             NaN      Mean Imputation   
 107  Random Forest       NaN            True      Mean Imputation   
 108  Random Forest       NaN             0.0      Mean Imputation   
 109  Random Forest       NaN            None      Mean Imputation   
 110  Random Forest       NaN            gini      Mean Imputation   
 111  Random Forest       NaN            None      Mean Imputation   
 112  Random Forest       NaN            sqrt      Mean Imputation   
 113  Random Forest       NaN            None      Mean Imputation   
 114  Random Forest       NaN            None      Mean Imputation   
 115  Random Forest       NaN             0.0      Mean Imputation   
 116  Random Forest       NaN               1      Mean Imputation   
 117  Random Forest       NaN               2      Mean Imputation   
 118  Random Forest       NaN             0.0      Mean Imputation   
 119  Random Forest       NaN             100      Mean Imputation   
 120  Random Forest       NaN            None      Mean Imputation   
 121  Random Forest       NaN           False      Mean Imputation   
 122  Random Forest       NaN            None      Mean Imputation   
 123  Random Forest       NaN               0      Mean Imputation   
 124  Random Forest       NaN           False      Mean Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 1    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 2    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 3    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 4    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 5    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 6    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 7    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 8    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 9    Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 10   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 11   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 12   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 13   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 14   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 15   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 16   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 17   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 18   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 19   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 20   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 21   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 22   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 23   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 24   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 25   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 26   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 27   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 28   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 29   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 30   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 31   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 32   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 33   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 34   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 35   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 36   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 37   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 38   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 39   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 40   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 41   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 42   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 43   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 44   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 45   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 46   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 47   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 48   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 49   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 50   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 51   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 52   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 53   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 54   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 55   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 56   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 57   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 58   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 59   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 60   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 61   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 62   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 63   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 64   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 65   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 66   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 67   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 68   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 69   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 70   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 71   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 72   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 73   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 74   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 75   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 76   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 77   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 78   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 79   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 80   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 81   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 82   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 83   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 84   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 85   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 86   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 87   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 88   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 89   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 90   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 91   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 92   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 93   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 94   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 95   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 96   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 97   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 98   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 99   Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 100  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 101  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 102  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 103  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 104  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 105  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 106  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 107  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 108  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 109  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 110  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 111  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 112  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 113  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 114  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 115  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 116  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 117  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 118  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 119  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 120  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 121  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 122  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 123  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  
 124  Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.899131             NaN    Median Imputation   
 1    Random Forest  0.123711             NaN    Median Imputation   
 2    Random Forest  0.101266             NaN    Median Imputation   
 3    Random Forest  0.111369             NaN    Median Imputation   
 4    Random Forest  0.614159             NaN    Median Imputation   
 5    Random Forest  0.210064             NaN    Median Imputation   
 6    Random Forest  0.228317             NaN    Median Imputation   
 7    Random Forest       NaN            True    Median Imputation   
 8    Random Forest       NaN             0.0    Median Imputation   
 9    Random Forest       NaN            None    Median Imputation   
 10   Random Forest       NaN            gini    Median Imputation   
 11   Random Forest       NaN            None    Median Imputation   
 12   Random Forest       NaN            sqrt    Median Imputation   
 13   Random Forest       NaN            None    Median Imputation   
 14   Random Forest       NaN            None    Median Imputation   
 15   Random Forest       NaN             0.0    Median Imputation   
 16   Random Forest       NaN               1    Median Imputation   
 17   Random Forest       NaN               2    Median Imputation   
 18   Random Forest       NaN             0.0    Median Imputation   
 19   Random Forest       NaN             100    Median Imputation   
 20   Random Forest       NaN            None    Median Imputation   
 21   Random Forest       NaN           False    Median Imputation   
 22   Random Forest       NaN            None    Median Imputation   
 23   Random Forest       NaN               0    Median Imputation   
 24   Random Forest       NaN           False    Median Imputation   
 25   Random Forest  0.903871             NaN    Median Imputation   
 26   Random Forest  0.111969             NaN    Median Imputation   
 27   Random Forest  0.176829             NaN    Median Imputation   
 28   Random Forest  0.137116             NaN    Median Imputation   
 29   Random Forest  0.627970             NaN    Median Imputation   
 30   Random Forest  0.237407             NaN    Median Imputation   
 31   Random Forest  0.255940             NaN    Median Imputation   
 32   Random Forest       NaN            True    Median Imputation   
 33   Random Forest       NaN             0.0    Median Imputation   
 34   Random Forest       NaN            None    Median Imputation   
 35   Random Forest       NaN            gini    Median Imputation   
 36   Random Forest       NaN            None    Median Imputation   
 37   Random Forest       NaN            sqrt    Median Imputation   
 38   Random Forest       NaN            None    Median Imputation   
 39   Random Forest       NaN            None    Median Imputation   
 40   Random Forest       NaN             0.0    Median Imputation   
 41   Random Forest       NaN               1    Median Imputation   
 42   Random Forest       NaN               2    Median Imputation   
 43   Random Forest       NaN             0.0    Median Imputation   
 44   Random Forest       NaN             100    Median Imputation   
 45   Random Forest       NaN            None    Median Imputation   
 46   Random Forest       NaN           False    Median Imputation   
 47   Random Forest       NaN            None    Median Imputation   
 48   Random Forest       NaN               0    Median Imputation   
 49   Random Forest       NaN           False    Median Imputation   
 50   Random Forest  0.902818             NaN    Median Imputation   
 51   Random Forest  0.107759             NaN    Median Imputation   
 52   Random Forest  0.133690             NaN    Median Imputation   
 53   Random Forest  0.119332             NaN    Median Imputation   
 54   Random Forest  0.597185             NaN    Median Imputation   
 55   Random Forest  0.198168             NaN    Median Imputation   
 56   Random Forest  0.194371             NaN    Median Imputation   
 57   Random Forest       NaN            True    Median Imputation   
 58   Random Forest       NaN             0.0    Median Imputation   
 59   Random Forest       NaN            None    Median Imputation   
 60   Random Forest       NaN            gini    Median Imputation   
 61   Random Forest       NaN            None    Median Imputation   
 62   Random Forest       NaN            sqrt    Median Imputation   
 63   Random Forest       NaN            None    Median Imputation   
 64   Random Forest       NaN            None    Median Imputation   
 65   Random Forest       NaN             0.0    Median Imputation   
 66   Random Forest       NaN               1    Median Imputation   
 67   Random Forest       NaN               2    Median Imputation   
 68   Random Forest       NaN             0.0    Median Imputation   
 69   Random Forest       NaN             100    Median Imputation   
 70   Random Forest       NaN            None    Median Imputation   
 71   Random Forest       NaN           False    Median Imputation   
 72   Random Forest       NaN            None    Median Imputation   
 73   Random Forest       NaN               0    Median Imputation   
 74   Random Forest       NaN           False    Median Imputation   
 75   Random Forest  0.896470             NaN    Median Imputation   
 76   Random Forest  0.104418             NaN    Median Imputation   
 77   Random Forest  0.132653             NaN    Median Imputation   
 78   Random Forest  0.116854             NaN    Median Imputation   
 79   Random Forest  0.583388             NaN    Median Imputation   
 80   Random Forest  0.164773             NaN    Median Imputation   
 81   Random Forest  0.166776             NaN    Median Imputation   
 82   Random Forest       NaN            True    Median Imputation   
 83   Random Forest       NaN             0.0    Median Imputation   
 84   Random Forest       NaN            None    Median Imputation   
 85   Random Forest       NaN            gini    Median Imputation   
 86   Random Forest       NaN            None    Median Imputation   
 87   Random Forest       NaN            sqrt    Median Imputation   
 88   Random Forest       NaN            None    Median Imputation   
 89   Random Forest       NaN            None    Median Imputation   
 90   Random Forest       NaN             0.0    Median Imputation   
 91   Random Forest       NaN               1    Median Imputation   
 92   Random Forest       NaN               2    Median Imputation   
 93   Random Forest       NaN             0.0    Median Imputation   
 94   Random Forest       NaN             100    Median Imputation   
 95   Random Forest       NaN            None    Median Imputation   
 96   Random Forest       NaN           False    Median Imputation   
 97   Random Forest       NaN            None    Median Imputation   
 98   Random Forest       NaN               0    Median Imputation   
 99   Random Forest       NaN           False    Median Imputation   
 100  Random Forest  0.892518             NaN    Median Imputation   
 101  Random Forest  0.141791             NaN    Median Imputation   
 102  Random Forest  0.175926             NaN    Median Imputation   
 103  Random Forest  0.157025             NaN    Median Imputation   
 104  Random Forest  0.599265             NaN    Median Imputation   
 105  Random Forest  0.229092             NaN    Median Imputation   
 106  Random Forest  0.198530             NaN    Median Imputation   
 107  Random Forest       NaN            True    Median Imputation   
 108  Random Forest       NaN             0.0    Median Imputation   
 109  Random Forest       NaN            None    Median Imputation   
 110  Random Forest       NaN            gini    Median Imputation   
 111  Random Forest       NaN            None    Median Imputation   
 112  Random Forest       NaN            sqrt    Median Imputation   
 113  Random Forest       NaN            None    Median Imputation   
 114  Random Forest       NaN            None    Median Imputation   
 115  Random Forest       NaN             0.0    Median Imputation   
 116  Random Forest       NaN               1    Median Imputation   
 117  Random Forest       NaN               2    Median Imputation   
 118  Random Forest       NaN             0.0    Median Imputation   
 119  Random Forest       NaN             100    Median Imputation   
 120  Random Forest       NaN            None    Median Imputation   
 121  Random Forest       NaN           False    Median Imputation   
 122  Random Forest       NaN            None    Median Imputation   
 123  Random Forest       NaN               0    Median Imputation   
 124  Random Forest       NaN           False    Median Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 1    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 2    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 3    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 4    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 5    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 6    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 7    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 8    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 9    Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 10   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 11   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 12   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 13   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 14   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 15   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 16   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 17   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 18   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 19   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 20   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 21   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 22   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 23   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 24   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 25   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 26   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 27   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 28   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 29   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 30   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 31   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 32   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 33   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 34   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 35   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 36   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 37   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 38   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 39   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 40   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 41   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 42   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 43   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 44   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 45   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 46   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 47   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 48   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 49   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 50   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 51   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 52   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 53   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 54   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 55   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 56   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 57   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 58   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 59   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 60   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 61   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 62   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 63   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 64   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 65   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 66   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 67   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 68   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 69   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 70   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 71   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 72   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 73   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 74   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 75   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 76   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 77   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 78   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 79   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 80   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 81   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 82   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 83   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 84   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 85   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 86   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 87   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 88   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 89   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 90   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 91   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 92   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 93   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 94   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 95   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 96   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 97   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 98   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 99   Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 100  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 101  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 102  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 103  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 104  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 105  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 106  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 107  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 108  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 109  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 110  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 111  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 112  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 113  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 114  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 115  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 116  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 117  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 118  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 119  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 120  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 121  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 122  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 123  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  
 124  Random Forest_Hybrid Sampling (SMOTEENN)_Media...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.903871             NaN      Zero Imputation   
 1    Random Forest  0.132184             NaN      Zero Imputation   
 2    Random Forest  0.097046             NaN      Zero Imputation   
 3    Random Forest  0.111922             NaN      Zero Imputation   
 4    Random Forest  0.638794             NaN      Zero Imputation   
 5    Random Forest  0.228166             NaN      Zero Imputation   
 6    Random Forest  0.277587             NaN      Zero Imputation   
 7    Random Forest       NaN            True      Zero Imputation   
 8    Random Forest       NaN             0.0      Zero Imputation   
 9    Random Forest       NaN            None      Zero Imputation   
 10   Random Forest       NaN            gini      Zero Imputation   
 11   Random Forest       NaN            None      Zero Imputation   
 12   Random Forest       NaN            sqrt      Zero Imputation   
 13   Random Forest       NaN            None      Zero Imputation   
 14   Random Forest       NaN            None      Zero Imputation   
 15   Random Forest       NaN             0.0      Zero Imputation   
 16   Random Forest       NaN               1      Zero Imputation   
 17   Random Forest       NaN               2      Zero Imputation   
 18   Random Forest       NaN             0.0      Zero Imputation   
 19   Random Forest       NaN             100      Zero Imputation   
 20   Random Forest       NaN            None      Zero Imputation   
 21   Random Forest       NaN           False      Zero Imputation   
 22   Random Forest       NaN            None      Zero Imputation   
 23   Random Forest       NaN               0      Zero Imputation   
 24   Random Forest       NaN           False      Zero Imputation   
 25   Random Forest  0.920200             NaN      Zero Imputation   
 26   Random Forest  0.116022             NaN      Zero Imputation   
 27   Random Forest  0.128049             NaN      Zero Imputation   
 28   Random Forest  0.121739             NaN      Zero Imputation   
 29   Random Forest  0.662665             NaN      Zero Imputation   
 30   Random Forest  0.280691             NaN      Zero Imputation   
 31   Random Forest  0.325329             NaN      Zero Imputation   
 32   Random Forest       NaN            True      Zero Imputation   
 33   Random Forest       NaN             0.0      Zero Imputation   
 34   Random Forest       NaN            None      Zero Imputation   
 35   Random Forest       NaN            gini      Zero Imputation   
 36   Random Forest       NaN            None      Zero Imputation   
 37   Random Forest       NaN            sqrt      Zero Imputation   
 38   Random Forest       NaN            None      Zero Imputation   
 39   Random Forest       NaN            None      Zero Imputation   
 40   Random Forest       NaN             0.0      Zero Imputation   
 41   Random Forest       NaN               1      Zero Imputation   
 42   Random Forest       NaN               2      Zero Imputation   
 43   Random Forest       NaN             0.0      Zero Imputation   
 44   Random Forest       NaN             100      Zero Imputation   
 45   Random Forest       NaN            None      Zero Imputation   
 46   Random Forest       NaN           False      Zero Imputation   
 47   Random Forest       NaN            None      Zero Imputation   
 48   Random Forest       NaN               0      Zero Imputation   
 49   Random Forest       NaN           False      Zero Imputation   
 50   Random Forest  0.909929             NaN      Zero Imputation   
 51   Random Forest  0.132701             NaN      Zero Imputation   
 52   Random Forest  0.149733             NaN      Zero Imputation   
 53   Random Forest  0.140704             NaN      Zero Imputation   
 54   Random Forest  0.668543             NaN      Zero Imputation   
 55   Random Forest  0.293364             NaN      Zero Imputation   
 56   Random Forest  0.337085             NaN      Zero Imputation   
 57   Random Forest       NaN            True      Zero Imputation   
 58   Random Forest       NaN             0.0      Zero Imputation   
 59   Random Forest       NaN            None      Zero Imputation   
 60   Random Forest       NaN            gini      Zero Imputation   
 61   Random Forest       NaN            None      Zero Imputation   
 62   Random Forest       NaN            sqrt      Zero Imputation   
 63   Random Forest       NaN            None      Zero Imputation   
 64   Random Forest       NaN            None      Zero Imputation   
 65   Random Forest       NaN             0.0      Zero Imputation   
 66   Random Forest       NaN               1      Zero Imputation   
 67   Random Forest       NaN               2      Zero Imputation   
 68   Random Forest       NaN             0.0      Zero Imputation   
 69   Random Forest       NaN             100      Zero Imputation   
 70   Random Forest       NaN            None      Zero Imputation   
 71   Random Forest       NaN           False      Zero Imputation   
 72   Random Forest       NaN            None      Zero Imputation   
 73   Random Forest       NaN               0      Zero Imputation   
 74   Random Forest       NaN           False      Zero Imputation   
 75   Random Forest  0.909115             NaN      Zero Imputation   
 76   Random Forest  0.109948             NaN      Zero Imputation   
 77   Random Forest  0.107143             NaN      Zero Imputation   
 78   Random Forest  0.108527             NaN      Zero Imputation   
 79   Random Forest  0.608021             NaN      Zero Imputation   
 80   Random Forest  0.201372             NaN      Zero Imputation   
 81   Random Forest  0.216042             NaN      Zero Imputation   
 82   Random Forest       NaN            True      Zero Imputation   
 83   Random Forest       NaN             0.0      Zero Imputation   
 84   Random Forest       NaN            None      Zero Imputation   
 85   Random Forest       NaN            gini      Zero Imputation   
 86   Random Forest       NaN            None      Zero Imputation   
 87   Random Forest       NaN            sqrt      Zero Imputation   
 88   Random Forest       NaN            None      Zero Imputation   
 89   Random Forest       NaN            None      Zero Imputation   
 90   Random Forest       NaN             0.0      Zero Imputation   
 91   Random Forest       NaN               1      Zero Imputation   
 92   Random Forest       NaN               2      Zero Imputation   
 93   Random Forest       NaN             0.0      Zero Imputation   
 94   Random Forest       NaN             100      Zero Imputation   
 95   Random Forest       NaN            None      Zero Imputation   
 96   Random Forest       NaN           False      Zero Imputation   
 97   Random Forest       NaN            None      Zero Imputation   
 98   Random Forest       NaN               0      Zero Imputation   
 99   Random Forest       NaN           False      Zero Imputation   
 100  Random Forest  0.906744             NaN      Zero Imputation   
 101  Random Forest  0.132979             NaN      Zero Imputation   
 102  Random Forest  0.115741             NaN      Zero Imputation   
 103  Random Forest  0.123762             NaN      Zero Imputation   
 104  Random Forest  0.610973             NaN      Zero Imputation   
 105  Random Forest  0.225455             NaN      Zero Imputation   
 106  Random Forest  0.221945             NaN      Zero Imputation   
 107  Random Forest       NaN            True      Zero Imputation   
 108  Random Forest       NaN             0.0      Zero Imputation   
 109  Random Forest       NaN            None      Zero Imputation   
 110  Random Forest       NaN            gini      Zero Imputation   
 111  Random Forest       NaN            None      Zero Imputation   
 112  Random Forest       NaN            sqrt      Zero Imputation   
 113  Random Forest       NaN            None      Zero Imputation   
 114  Random Forest       NaN            None      Zero Imputation   
 115  Random Forest       NaN             0.0      Zero Imputation   
 116  Random Forest       NaN               1      Zero Imputation   
 117  Random Forest       NaN               2      Zero Imputation   
 118  Random Forest       NaN             0.0      Zero Imputation   
 119  Random Forest       NaN             100      Zero Imputation   
 120  Random Forest       NaN            None      Zero Imputation   
 121  Random Forest       NaN           False      Zero Imputation   
 122  Random Forest       NaN            None      Zero Imputation   
 123  Random Forest       NaN               0      Zero Imputation   
 124  Random Forest       NaN           False      Zero Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 1    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 2    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 3    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 4    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 5    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 6    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 7    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 8    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 9    Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 10   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 11   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 12   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 13   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 14   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 15   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 16   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 17   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 18   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 19   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 20   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 21   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 22   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 23   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 24   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 25   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 26   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 27   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 28   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 29   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 30   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 31   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 32   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 33   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 34   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 35   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 36   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 37   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 38   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 39   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 40   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 41   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 42   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 43   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 44   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 45   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 46   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 47   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 48   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 49   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 50   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 51   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 52   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 53   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 54   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 55   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 56   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 57   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 58   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 59   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 60   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 61   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 62   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 63   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 64   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 65   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 66   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 67   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 68   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 69   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 70   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 71   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 72   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 73   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 74   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 75   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 76   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 77   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 78   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 79   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 80   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 81   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 82   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 83   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 84   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 85   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 86   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 87   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 88   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 89   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 90   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 91   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 92   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 93   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 94   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 95   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 96   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 97   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 98   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 99   Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 100  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 101  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 102  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 103  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 104  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 105  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 106  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 107  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 108  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 109  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 110  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 111  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 112  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 113  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 114  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 115  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 116  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 117  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 118  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 119  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 120  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 121  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 122  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 123  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  
 124  Random Forest_Hybrid Sampling (SMOTETomek)_Zer...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.904662             NaN      Mean Imputation   
 1    Random Forest  0.121212             NaN      Mean Imputation   
 2    Random Forest  0.084388             NaN      Mean Imputation   
 3    Random Forest  0.099502             NaN      Mean Imputation   
 4    Random Forest  0.631506             NaN      Mean Imputation   
 5    Random Forest  0.233609             NaN      Mean Imputation   
 6    Random Forest  0.263011             NaN      Mean Imputation   
 7    Random Forest       NaN            True      Mean Imputation   
 8    Random Forest       NaN             0.0      Mean Imputation   
 9    Random Forest       NaN            None      Mean Imputation   
 10   Random Forest       NaN            gini      Mean Imputation   
 11   Random Forest       NaN            None      Mean Imputation   
 12   Random Forest       NaN            sqrt      Mean Imputation   
 13   Random Forest       NaN            None      Mean Imputation   
 14   Random Forest       NaN            None      Mean Imputation   
 15   Random Forest       NaN             0.0      Mean Imputation   
 16   Random Forest       NaN               1      Mean Imputation   
 17   Random Forest       NaN               2      Mean Imputation   
 18   Random Forest       NaN             0.0      Mean Imputation   
 19   Random Forest       NaN             100      Mean Imputation   
 20   Random Forest       NaN            None      Mean Imputation   
 21   Random Forest       NaN           False      Mean Imputation   
 22   Random Forest       NaN            None      Mean Imputation   
 23   Random Forest       NaN               0      Mean Imputation   
 24   Random Forest       NaN           False      Mean Imputation   
 25   Random Forest  0.920727             NaN      Mean Imputation   
 26   Random Forest  0.125683             NaN      Mean Imputation   
 27   Random Forest  0.140244             NaN      Mean Imputation   
 28   Random Forest  0.132565             NaN      Mean Imputation   
 29   Random Forest  0.654877             NaN      Mean Imputation   
 30   Random Forest  0.283868             NaN      Mean Imputation   
 31   Random Forest  0.309754             NaN      Mean Imputation   
 32   Random Forest       NaN            True      Mean Imputation   
 33   Random Forest       NaN             0.0      Mean Imputation   
 34   Random Forest       NaN            None      Mean Imputation   
 35   Random Forest       NaN            gini      Mean Imputation   
 36   Random Forest       NaN            None      Mean Imputation   
 37   Random Forest       NaN            sqrt      Mean Imputation   
 38   Random Forest       NaN            None      Mean Imputation   
 39   Random Forest       NaN            None      Mean Imputation   
 40   Random Forest       NaN             0.0      Mean Imputation   
 41   Random Forest       NaN               1      Mean Imputation   
 42   Random Forest       NaN               2      Mean Imputation   
 43   Random Forest       NaN             0.0      Mean Imputation   
 44   Random Forest       NaN             100      Mean Imputation   
 45   Random Forest       NaN            None      Mean Imputation   
 46   Random Forest       NaN           False      Mean Imputation   
 47   Random Forest       NaN            None      Mean Imputation   
 48   Random Forest       NaN               0      Mean Imputation   
 49   Random Forest       NaN           False      Mean Imputation   
 50   Random Forest  0.915723             NaN      Mean Imputation   
 51   Random Forest  0.144385             NaN      Mean Imputation   
 52   Random Forest  0.144385             NaN      Mean Imputation   
 53   Random Forest  0.144385             NaN      Mean Imputation   
 54   Random Forest  0.667364             NaN      Mean Imputation   
 55   Random Forest  0.279189             NaN      Mean Imputation   
 56   Random Forest  0.334728             NaN      Mean Imputation   
 57   Random Forest       NaN            True      Mean Imputation   
 58   Random Forest       NaN             0.0      Mean Imputation   
 59   Random Forest       NaN            None      Mean Imputation   
 60   Random Forest       NaN            gini      Mean Imputation   
 61   Random Forest       NaN            None      Mean Imputation   
 62   Random Forest       NaN            sqrt      Mean Imputation   
 63   Random Forest       NaN            None      Mean Imputation   
 64   Random Forest       NaN            None      Mean Imputation   
 65   Random Forest       NaN             0.0      Mean Imputation   
 66   Random Forest       NaN               1      Mean Imputation   
 67   Random Forest       NaN               2      Mean Imputation   
 68   Random Forest       NaN             0.0      Mean Imputation   
 69   Random Forest       NaN             100      Mean Imputation   
 70   Random Forest       NaN            None      Mean Imputation   
 71   Random Forest       NaN           False      Mean Imputation   
 72   Random Forest       NaN            None      Mean Imputation   
 73   Random Forest       NaN               0      Mean Imputation   
 74   Random Forest       NaN           False      Mean Imputation   
 75   Random Forest  0.912276             NaN      Mean Imputation   
 76   Random Forest  0.141361             NaN      Mean Imputation   
 77   Random Forest  0.137755             NaN      Mean Imputation   
 78   Random Forest  0.139535             NaN      Mean Imputation   
 79   Random Forest  0.621750             NaN      Mean Imputation   
 80   Random Forest  0.218044             NaN      Mean Imputation   
 81   Random Forest  0.243499             NaN      Mean Imputation   
 82   Random Forest       NaN            True      Mean Imputation   
 83   Random Forest       NaN             0.0      Mean Imputation   
 84   Random Forest       NaN            None      Mean Imputation   
 85   Random Forest       NaN            gini      Mean Imputation   
 86   Random Forest       NaN            None      Mean Imputation   
 87   Random Forest       NaN            sqrt      Mean Imputation   
 88   Random Forest       NaN            None      Mean Imputation   
 89   Random Forest       NaN            None      Mean Imputation   
 90   Random Forest       NaN             0.0      Mean Imputation   
 91   Random Forest       NaN               1      Mean Imputation   
 92   Random Forest       NaN               2      Mean Imputation   
 93   Random Forest       NaN             0.0      Mean Imputation   
 94   Random Forest       NaN             100      Mean Imputation   
 95   Random Forest       NaN            None      Mean Imputation   
 96   Random Forest       NaN           False      Mean Imputation   
 97   Random Forest       NaN            None      Mean Imputation   
 98   Random Forest       NaN               0      Mean Imputation   
 99   Random Forest       NaN           False      Mean Imputation   
 100  Random Forest  0.908325             NaN      Mean Imputation   
 101  Random Forest  0.159794             NaN      Mean Imputation   
 102  Random Forest  0.143519             NaN      Mean Imputation   
 103  Random Forest  0.151220             NaN      Mean Imputation   
 104  Random Forest  0.608478             NaN      Mean Imputation   
 105  Random Forest  0.186251             NaN      Mean Imputation   
 106  Random Forest  0.216956             NaN      Mean Imputation   
 107  Random Forest       NaN            True      Mean Imputation   
 108  Random Forest       NaN             0.0      Mean Imputation   
 109  Random Forest       NaN            None      Mean Imputation   
 110  Random Forest       NaN            gini      Mean Imputation   
 111  Random Forest       NaN            None      Mean Imputation   
 112  Random Forest       NaN            sqrt      Mean Imputation   
 113  Random Forest       NaN            None      Mean Imputation   
 114  Random Forest       NaN            None      Mean Imputation   
 115  Random Forest       NaN             0.0      Mean Imputation   
 116  Random Forest       NaN               1      Mean Imputation   
 117  Random Forest       NaN               2      Mean Imputation   
 118  Random Forest       NaN             0.0      Mean Imputation   
 119  Random Forest       NaN             100      Mean Imputation   
 120  Random Forest       NaN            None      Mean Imputation   
 121  Random Forest       NaN           False      Mean Imputation   
 122  Random Forest       NaN            None      Mean Imputation   
 123  Random Forest       NaN               0      Mean Imputation   
 124  Random Forest       NaN           False      Mean Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 1    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 2    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 3    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 4    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 5    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 6    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 7    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 8    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 9    Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 10   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 11   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 12   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 13   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 14   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 15   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 16   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 17   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 18   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 19   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 20   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 21   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 22   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 23   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 24   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 25   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 26   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 27   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 28   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 29   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 30   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 31   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 32   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 33   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 34   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 35   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 36   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 37   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 38   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 39   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 40   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 41   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 42   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 43   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 44   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 45   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 46   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 47   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 48   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 49   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 50   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 51   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 52   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 53   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 54   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 55   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 56   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 57   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 58   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 59   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 60   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 61   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 62   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 63   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 64   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 65   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 66   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 67   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 68   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 69   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 70   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 71   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 72   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 73   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 74   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 75   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 76   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 77   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 78   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 79   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 80   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 81   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 82   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 83   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 84   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 85   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 86   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 87   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 88   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 89   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 90   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 91   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 92   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 93   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 94   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 95   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 96   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 97   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 98   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 99   Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 100  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 101  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 102  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 103  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 104  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 105  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 106  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 107  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 108  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 109  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 110  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 111  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 112  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 113  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 114  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 115  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 116  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 117  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 118  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 119  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 120  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 121  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 122  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 123  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  
 124  Random Forest_Hybrid Sampling (SMOTETomek)_Mea...  ,
          Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    Random Forest  0.903608             NaN    Median Imputation   
 1    Random Forest  0.109091             NaN    Median Imputation   
 2    Random Forest  0.075949             NaN    Median Imputation   
 3    Random Forest  0.089552             NaN    Median Imputation   
 4    Random Forest  0.616043             NaN    Median Imputation   
 5    Random Forest  0.187701             NaN    Median Imputation   
 6    Random Forest  0.232086             NaN    Median Imputation   
 7    Random Forest       NaN            True    Median Imputation   
 8    Random Forest       NaN             0.0    Median Imputation   
 9    Random Forest       NaN            None    Median Imputation   
 10   Random Forest       NaN            gini    Median Imputation   
 11   Random Forest       NaN            None    Median Imputation   
 12   Random Forest       NaN            sqrt    Median Imputation   
 13   Random Forest       NaN            None    Median Imputation   
 14   Random Forest       NaN            None    Median Imputation   
 15   Random Forest       NaN             0.0    Median Imputation   
 16   Random Forest       NaN               1    Median Imputation   
 17   Random Forest       NaN               2    Median Imputation   
 18   Random Forest       NaN             0.0    Median Imputation   
 19   Random Forest       NaN             100    Median Imputation   
 20   Random Forest       NaN            None    Median Imputation   
 21   Random Forest       NaN           False    Median Imputation   
 22   Random Forest       NaN            None    Median Imputation   
 23   Random Forest       NaN               0    Median Imputation   
 24   Random Forest       NaN           False    Median Imputation   
 25   Random Forest  0.916776             NaN    Median Imputation   
 26   Random Forest  0.108247             NaN    Median Imputation   
 27   Random Forest  0.128049             NaN    Median Imputation   
 28   Random Forest  0.117318             NaN    Median Imputation   
 29   Random Forest  0.644780             NaN    Median Imputation   
 30   Random Forest  0.230455             NaN    Median Imputation   
 31   Random Forest  0.289559             NaN    Median Imputation   
 32   Random Forest       NaN            True    Median Imputation   
 33   Random Forest       NaN             0.0    Median Imputation   
 34   Random Forest       NaN            None    Median Imputation   
 35   Random Forest       NaN            gini    Median Imputation   
 36   Random Forest       NaN            None    Median Imputation   
 37   Random Forest       NaN            sqrt    Median Imputation   
 38   Random Forest       NaN            None    Median Imputation   
 39   Random Forest       NaN            None    Median Imputation   
 40   Random Forest       NaN             0.0    Median Imputation   
 41   Random Forest       NaN               1    Median Imputation   
 42   Random Forest       NaN               2    Median Imputation   
 43   Random Forest       NaN             0.0    Median Imputation   
 44   Random Forest       NaN             100    Median Imputation   
 45   Random Forest       NaN            None    Median Imputation   
 46   Random Forest       NaN           False    Median Imputation   
 47   Random Forest       NaN            None    Median Imputation   
 48   Random Forest       NaN               0    Median Imputation   
 49   Random Forest       NaN           False    Median Imputation   
 50   Random Forest  0.916250             NaN    Median Imputation   
 51   Random Forest  0.149733             NaN    Median Imputation   
 52   Random Forest  0.149733             NaN    Median Imputation   
 53   Random Forest  0.149733             NaN    Median Imputation   
 54   Random Forest  0.655823             NaN    Median Imputation   
 55   Random Forest  0.281935             NaN    Median Imputation   
 56   Random Forest  0.311646             NaN    Median Imputation   
 57   Random Forest       NaN            True    Median Imputation   
 58   Random Forest       NaN             0.0    Median Imputation   
 59   Random Forest       NaN            None    Median Imputation   
 60   Random Forest       NaN            gini    Median Imputation   
 61   Random Forest       NaN            None    Median Imputation   
 62   Random Forest       NaN            sqrt    Median Imputation   
 63   Random Forest       NaN            None    Median Imputation   
 64   Random Forest       NaN            None    Median Imputation   
 65   Random Forest       NaN             0.0    Median Imputation   
 66   Random Forest       NaN               1    Median Imputation   
 67   Random Forest       NaN               2    Median Imputation   
 68   Random Forest       NaN             0.0    Median Imputation   
 69   Random Forest       NaN             100    Median Imputation   
 70   Random Forest       NaN            None    Median Imputation   
 71   Random Forest       NaN           False    Median Imputation   
 72   Random Forest       NaN            None    Median Imputation   
 73   Random Forest       NaN               0    Median Imputation   
 74   Random Forest       NaN           False    Median Imputation   
 75   Random Forest  0.913330             NaN    Median Imputation   
 76   Random Forest  0.144385             NaN    Median Imputation   
 77   Random Forest  0.137755             NaN    Median Imputation   
 78   Random Forest  0.140992             NaN    Median Imputation   
 79   Random Forest  0.597423             NaN    Median Imputation   
 80   Random Forest  0.200675             NaN    Median Imputation   
 81   Random Forest  0.194846             NaN    Median Imputation   
 82   Random Forest       NaN            True    Median Imputation   
 83   Random Forest       NaN             0.0    Median Imputation   
 84   Random Forest       NaN            None    Median Imputation   
 85   Random Forest       NaN            gini    Median Imputation   
 86   Random Forest       NaN            None    Median Imputation   
 87   Random Forest       NaN            sqrt    Median Imputation   
 88   Random Forest       NaN            None    Median Imputation   
 89   Random Forest       NaN            None    Median Imputation   
 90   Random Forest       NaN             0.0    Median Imputation   
 91   Random Forest       NaN               1    Median Imputation   
 92   Random Forest       NaN               2    Median Imputation   
 93   Random Forest       NaN             0.0    Median Imputation   
 94   Random Forest       NaN             100    Median Imputation   
 95   Random Forest       NaN            None    Median Imputation   
 96   Random Forest       NaN           False    Median Imputation   
 97   Random Forest       NaN            None    Median Imputation   
 98   Random Forest       NaN               0    Median Imputation   
 99   Random Forest       NaN           False    Median Imputation   
 100  Random Forest  0.904900             NaN    Median Imputation   
 101  Random Forest  0.146341             NaN    Median Imputation   
 102  Random Forest  0.138889             NaN    Median Imputation   
 103  Random Forest  0.142518             NaN    Median Imputation   
 104  Random Forest  0.619271             NaN    Median Imputation   
 105  Random Forest  0.211799             NaN    Median Imputation   
 106  Random Forest  0.238541             NaN    Median Imputation   
 107  Random Forest       NaN            True    Median Imputation   
 108  Random Forest       NaN             0.0    Median Imputation   
 109  Random Forest       NaN            None    Median Imputation   
 110  Random Forest       NaN            gini    Median Imputation   
 111  Random Forest       NaN            None    Median Imputation   
 112  Random Forest       NaN            sqrt    Median Imputation   
 113  Random Forest       NaN            None    Median Imputation   
 114  Random Forest       NaN            None    Median Imputation   
 115  Random Forest       NaN             0.0    Median Imputation   
 116  Random Forest       NaN               1    Median Imputation   
 117  Random Forest       NaN               2    Median Imputation   
 118  Random Forest       NaN             0.0    Median Imputation   
 119  Random Forest       NaN             100    Median Imputation   
 120  Random Forest       NaN            None    Median Imputation   
 121  Random Forest       NaN           False    Median Imputation   
 122  Random Forest       NaN            None    Median Imputation   
 123  Random Forest       NaN               0    Median Imputation   
 124  Random Forest       NaN           False    Median Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 1    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 2    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 3    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 4    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 5    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 6    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 7    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 8    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 9    Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 10   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 11   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 12   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 13   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 14   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 15   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 16   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 17   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 18   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 19   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 20   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 21   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 22   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 23   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 24   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 25   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 26   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 27   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 28   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 29   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 30   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 31   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 32   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 33   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 34   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 35   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 36   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 37   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 38   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 39   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 40   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 41   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 42   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 43   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 44   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 45   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 46   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 47   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 48   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 49   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 50   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 51   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 52   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 53   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 54   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 55   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 56   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 57   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 58   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 59   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 60   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 61   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 62   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 63   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 64   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 65   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 66   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 67   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 68   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 69   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 70   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 71   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 72   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 73   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 74   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 75   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 76   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 77   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 78   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 79   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 80   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 81   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 82   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 83   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 84   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 85   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 86   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 87   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 88   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 89   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 90   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 91   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 92   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 93   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 94   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 95   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 96   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 97   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 98   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 99   Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 100  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 101  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 102  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 103  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 104  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 105  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 106  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 107  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 108  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 109  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 110  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 111  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 112  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 113  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 114  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 115  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 116  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 117  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 118  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 119  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 120  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 121  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 122  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 123  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  
 124  Random Forest_Hybrid Sampling (SMOTETomek)_Med...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.710034             NaN   
 1    Gradient Boosting Machines (GBM)  0.116341             NaN   
 2    Gradient Boosting Machines (GBM)  0.552743             NaN   
 3    Gradient Boosting Machines (GBM)  0.192223             NaN   
 4    Gradient Boosting Machines (GBM)  0.692938             NaN   
 5    Gradient Boosting Machines (GBM)  0.307039             NaN   
 6    Gradient Boosting Machines (GBM)  0.385876             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.706084             NaN   
 28   Gradient Boosting Machines (GBM)  0.082456             NaN   
 29   Gradient Boosting Machines (GBM)  0.573171             NaN   
 30   Gradient Boosting Machines (GBM)  0.144172             NaN   
 31   Gradient Boosting Machines (GBM)  0.713509             NaN   
 32   Gradient Boosting Machines (GBM)  0.339367             NaN   
 33   Gradient Boosting Machines (GBM)  0.427019             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.681064             NaN   
 55   Gradient Boosting Machines (GBM)  0.089744             NaN   
 56   Gradient Boosting Machines (GBM)  0.598930             NaN   
 57   Gradient Boosting Machines (GBM)  0.156098             NaN   
 58   Gradient Boosting Machines (GBM)  0.695709             NaN   
 59   Gradient Boosting Machines (GBM)  0.299498             NaN   
 60   Gradient Boosting Machines (GBM)  0.391419             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.685985             NaN   
 82   Gradient Boosting Machines (GBM)  0.095779             NaN   
 83   Gradient Boosting Machines (GBM)  0.602041             NaN   
 84   Gradient Boosting Machines (GBM)  0.165266             NaN   
 85   Gradient Boosting Machines (GBM)  0.694953             NaN   
 86   Gradient Boosting Machines (GBM)  0.310408             NaN   
 87   Gradient Boosting Machines (GBM)  0.389906             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.713383             NaN   
 109  Gradient Boosting Machines (GBM)  0.119546             NaN   
 110  Gradient Boosting Machines (GBM)  0.634259             NaN   
 111  Gradient Boosting Machines (GBM)  0.201175             NaN   
 112  Gradient Boosting Machines (GBM)  0.722514             NaN   
 113  Gradient Boosting Machines (GBM)  0.362435             NaN   
 114  Gradient Boosting Machines (GBM)  0.445029             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Zero Imputation              Oversampling   
 1        Zero Imputation              Oversampling   
 2        Zero Imputation              Oversampling   
 3        Zero Imputation              Oversampling   
 4        Zero Imputation              Oversampling   
 5        Zero Imputation              Oversampling   
 6        Zero Imputation              Oversampling   
 7        Zero Imputation              Oversampling   
 8        Zero Imputation              Oversampling   
 9        Zero Imputation              Oversampling   
 10       Zero Imputation              Oversampling   
 11       Zero Imputation              Oversampling   
 12       Zero Imputation              Oversampling   
 13       Zero Imputation              Oversampling   
 14       Zero Imputation              Oversampling   
 15       Zero Imputation              Oversampling   
 16       Zero Imputation              Oversampling   
 17       Zero Imputation              Oversampling   
 18       Zero Imputation              Oversampling   
 19       Zero Imputation              Oversampling   
 20       Zero Imputation              Oversampling   
 21       Zero Imputation              Oversampling   
 22       Zero Imputation              Oversampling   
 23       Zero Imputation              Oversampling   
 24       Zero Imputation              Oversampling   
 25       Zero Imputation              Oversampling   
 26       Zero Imputation              Oversampling   
 27       Zero Imputation              Oversampling   
 28       Zero Imputation              Oversampling   
 29       Zero Imputation              Oversampling   
 30       Zero Imputation              Oversampling   
 31       Zero Imputation              Oversampling   
 32       Zero Imputation              Oversampling   
 33       Zero Imputation              Oversampling   
 34       Zero Imputation              Oversampling   
 35       Zero Imputation              Oversampling   
 36       Zero Imputation              Oversampling   
 37       Zero Imputation              Oversampling   
 38       Zero Imputation              Oversampling   
 39       Zero Imputation              Oversampling   
 40       Zero Imputation              Oversampling   
 41       Zero Imputation              Oversampling   
 42       Zero Imputation              Oversampling   
 43       Zero Imputation              Oversampling   
 44       Zero Imputation              Oversampling   
 45       Zero Imputation              Oversampling   
 46       Zero Imputation              Oversampling   
 47       Zero Imputation              Oversampling   
 48       Zero Imputation              Oversampling   
 49       Zero Imputation              Oversampling   
 50       Zero Imputation              Oversampling   
 51       Zero Imputation              Oversampling   
 52       Zero Imputation              Oversampling   
 53       Zero Imputation              Oversampling   
 54       Zero Imputation              Oversampling   
 55       Zero Imputation              Oversampling   
 56       Zero Imputation              Oversampling   
 57       Zero Imputation              Oversampling   
 58       Zero Imputation              Oversampling   
 59       Zero Imputation              Oversampling   
 60       Zero Imputation              Oversampling   
 61       Zero Imputation              Oversampling   
 62       Zero Imputation              Oversampling   
 63       Zero Imputation              Oversampling   
 64       Zero Imputation              Oversampling   
 65       Zero Imputation              Oversampling   
 66       Zero Imputation              Oversampling   
 67       Zero Imputation              Oversampling   
 68       Zero Imputation              Oversampling   
 69       Zero Imputation              Oversampling   
 70       Zero Imputation              Oversampling   
 71       Zero Imputation              Oversampling   
 72       Zero Imputation              Oversampling   
 73       Zero Imputation              Oversampling   
 74       Zero Imputation              Oversampling   
 75       Zero Imputation              Oversampling   
 76       Zero Imputation              Oversampling   
 77       Zero Imputation              Oversampling   
 78       Zero Imputation              Oversampling   
 79       Zero Imputation              Oversampling   
 80       Zero Imputation              Oversampling   
 81       Zero Imputation              Oversampling   
 82       Zero Imputation              Oversampling   
 83       Zero Imputation              Oversampling   
 84       Zero Imputation              Oversampling   
 85       Zero Imputation              Oversampling   
 86       Zero Imputation              Oversampling   
 87       Zero Imputation              Oversampling   
 88       Zero Imputation              Oversampling   
 89       Zero Imputation              Oversampling   
 90       Zero Imputation              Oversampling   
 91       Zero Imputation              Oversampling   
 92       Zero Imputation              Oversampling   
 93       Zero Imputation              Oversampling   
 94       Zero Imputation              Oversampling   
 95       Zero Imputation              Oversampling   
 96       Zero Imputation              Oversampling   
 97       Zero Imputation              Oversampling   
 98       Zero Imputation              Oversampling   
 99       Zero Imputation              Oversampling   
 100      Zero Imputation              Oversampling   
 101      Zero Imputation              Oversampling   
 102      Zero Imputation              Oversampling   
 103      Zero Imputation              Oversampling   
 104      Zero Imputation              Oversampling   
 105      Zero Imputation              Oversampling   
 106      Zero Imputation              Oversampling   
 107      Zero Imputation              Oversampling   
 108      Zero Imputation              Oversampling   
 109      Zero Imputation              Oversampling   
 110      Zero Imputation              Oversampling   
 111      Zero Imputation              Oversampling   
 112      Zero Imputation              Oversampling   
 113      Zero Imputation              Oversampling   
 114      Zero Imputation              Oversampling   
 115      Zero Imputation              Oversampling   
 116      Zero Imputation              Oversampling   
 117      Zero Imputation              Oversampling   
 118      Zero Imputation              Oversampling   
 119      Zero Imputation              Oversampling   
 120      Zero Imputation              Oversampling   
 121      Zero Imputation              Oversampling   
 122      Zero Imputation              Oversampling   
 123      Zero Imputation              Oversampling   
 124      Zero Imputation              Oversampling   
 125      Zero Imputation              Oversampling   
 126      Zero Imputation              Oversampling   
 127      Zero Imputation              Oversampling   
 128      Zero Imputation              Oversampling   
 129      Zero Imputation              Oversampling   
 130      Zero Imputation              Oversampling   
 131      Zero Imputation              Oversampling   
 132      Zero Imputation              Oversampling   
 133      Zero Imputation              Oversampling   
 134      Zero Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Oversampling_...  
 1    Gradient Boosting Machines (GBM)_Oversampling_...  
 2    Gradient Boosting Machines (GBM)_Oversampling_...  
 3    Gradient Boosting Machines (GBM)_Oversampling_...  
 4    Gradient Boosting Machines (GBM)_Oversampling_...  
 5    Gradient Boosting Machines (GBM)_Oversampling_...  
 6    Gradient Boosting Machines (GBM)_Oversampling_...  
 7    Gradient Boosting Machines (GBM)_Oversampling_...  
 8    Gradient Boosting Machines (GBM)_Oversampling_...  
 9    Gradient Boosting Machines (GBM)_Oversampling_...  
 10   Gradient Boosting Machines (GBM)_Oversampling_...  
 11   Gradient Boosting Machines (GBM)_Oversampling_...  
 12   Gradient Boosting Machines (GBM)_Oversampling_...  
 13   Gradient Boosting Machines (GBM)_Oversampling_...  
 14   Gradient Boosting Machines (GBM)_Oversampling_...  
 15   Gradient Boosting Machines (GBM)_Oversampling_...  
 16   Gradient Boosting Machines (GBM)_Oversampling_...  
 17   Gradient Boosting Machines (GBM)_Oversampling_...  
 18   Gradient Boosting Machines (GBM)_Oversampling_...  
 19   Gradient Boosting Machines (GBM)_Oversampling_...  
 20   Gradient Boosting Machines (GBM)_Oversampling_...  
 21   Gradient Boosting Machines (GBM)_Oversampling_...  
 22   Gradient Boosting Machines (GBM)_Oversampling_...  
 23   Gradient Boosting Machines (GBM)_Oversampling_...  
 24   Gradient Boosting Machines (GBM)_Oversampling_...  
 25   Gradient Boosting Machines (GBM)_Oversampling_...  
 26   Gradient Boosting Machines (GBM)_Oversampling_...  
 27   Gradient Boosting Machines (GBM)_Oversampling_...  
 28   Gradient Boosting Machines (GBM)_Oversampling_...  
 29   Gradient Boosting Machines (GBM)_Oversampling_...  
 30   Gradient Boosting Machines (GBM)_Oversampling_...  
 31   Gradient Boosting Machines (GBM)_Oversampling_...  
 32   Gradient Boosting Machines (GBM)_Oversampling_...  
 33   Gradient Boosting Machines (GBM)_Oversampling_...  
 34   Gradient Boosting Machines (GBM)_Oversampling_...  
 35   Gradient Boosting Machines (GBM)_Oversampling_...  
 36   Gradient Boosting Machines (GBM)_Oversampling_...  
 37   Gradient Boosting Machines (GBM)_Oversampling_...  
 38   Gradient Boosting Machines (GBM)_Oversampling_...  
 39   Gradient Boosting Machines (GBM)_Oversampling_...  
 40   Gradient Boosting Machines (GBM)_Oversampling_...  
 41   Gradient Boosting Machines (GBM)_Oversampling_...  
 42   Gradient Boosting Machines (GBM)_Oversampling_...  
 43   Gradient Boosting Machines (GBM)_Oversampling_...  
 44   Gradient Boosting Machines (GBM)_Oversampling_...  
 45   Gradient Boosting Machines (GBM)_Oversampling_...  
 46   Gradient Boosting Machines (GBM)_Oversampling_...  
 47   Gradient Boosting Machines (GBM)_Oversampling_...  
 48   Gradient Boosting Machines (GBM)_Oversampling_...  
 49   Gradient Boosting Machines (GBM)_Oversampling_...  
 50   Gradient Boosting Machines (GBM)_Oversampling_...  
 51   Gradient Boosting Machines (GBM)_Oversampling_...  
 52   Gradient Boosting Machines (GBM)_Oversampling_...  
 53   Gradient Boosting Machines (GBM)_Oversampling_...  
 54   Gradient Boosting Machines (GBM)_Oversampling_...  
 55   Gradient Boosting Machines (GBM)_Oversampling_...  
 56   Gradient Boosting Machines (GBM)_Oversampling_...  
 57   Gradient Boosting Machines (GBM)_Oversampling_...  
 58   Gradient Boosting Machines (GBM)_Oversampling_...  
 59   Gradient Boosting Machines (GBM)_Oversampling_...  
 60   Gradient Boosting Machines (GBM)_Oversampling_...  
 61   Gradient Boosting Machines (GBM)_Oversampling_...  
 62   Gradient Boosting Machines (GBM)_Oversampling_...  
 63   Gradient Boosting Machines (GBM)_Oversampling_...  
 64   Gradient Boosting Machines (GBM)_Oversampling_...  
 65   Gradient Boosting Machines (GBM)_Oversampling_...  
 66   Gradient Boosting Machines (GBM)_Oversampling_...  
 67   Gradient Boosting Machines (GBM)_Oversampling_...  
 68   Gradient Boosting Machines (GBM)_Oversampling_...  
 69   Gradient Boosting Machines (GBM)_Oversampling_...  
 70   Gradient Boosting Machines (GBM)_Oversampling_...  
 71   Gradient Boosting Machines (GBM)_Oversampling_...  
 72   Gradient Boosting Machines (GBM)_Oversampling_...  
 73   Gradient Boosting Machines (GBM)_Oversampling_...  
 74   Gradient Boosting Machines (GBM)_Oversampling_...  
 75   Gradient Boosting Machines (GBM)_Oversampling_...  
 76   Gradient Boosting Machines (GBM)_Oversampling_...  
 77   Gradient Boosting Machines (GBM)_Oversampling_...  
 78   Gradient Boosting Machines (GBM)_Oversampling_...  
 79   Gradient Boosting Machines (GBM)_Oversampling_...  
 80   Gradient Boosting Machines (GBM)_Oversampling_...  
 81   Gradient Boosting Machines (GBM)_Oversampling_...  
 82   Gradient Boosting Machines (GBM)_Oversampling_...  
 83   Gradient Boosting Machines (GBM)_Oversampling_...  
 84   Gradient Boosting Machines (GBM)_Oversampling_...  
 85   Gradient Boosting Machines (GBM)_Oversampling_...  
 86   Gradient Boosting Machines (GBM)_Oversampling_...  
 87   Gradient Boosting Machines (GBM)_Oversampling_...  
 88   Gradient Boosting Machines (GBM)_Oversampling_...  
 89   Gradient Boosting Machines (GBM)_Oversampling_...  
 90   Gradient Boosting Machines (GBM)_Oversampling_...  
 91   Gradient Boosting Machines (GBM)_Oversampling_...  
 92   Gradient Boosting Machines (GBM)_Oversampling_...  
 93   Gradient Boosting Machines (GBM)_Oversampling_...  
 94   Gradient Boosting Machines (GBM)_Oversampling_...  
 95   Gradient Boosting Machines (GBM)_Oversampling_...  
 96   Gradient Boosting Machines (GBM)_Oversampling_...  
 97   Gradient Boosting Machines (GBM)_Oversampling_...  
 98   Gradient Boosting Machines (GBM)_Oversampling_...  
 99   Gradient Boosting Machines (GBM)_Oversampling_...  
 100  Gradient Boosting Machines (GBM)_Oversampling_...  
 101  Gradient Boosting Machines (GBM)_Oversampling_...  
 102  Gradient Boosting Machines (GBM)_Oversampling_...  
 103  Gradient Boosting Machines (GBM)_Oversampling_...  
 104  Gradient Boosting Machines (GBM)_Oversampling_...  
 105  Gradient Boosting Machines (GBM)_Oversampling_...  
 106  Gradient Boosting Machines (GBM)_Oversampling_...  
 107  Gradient Boosting Machines (GBM)_Oversampling_...  
 108  Gradient Boosting Machines (GBM)_Oversampling_...  
 109  Gradient Boosting Machines (GBM)_Oversampling_...  
 110  Gradient Boosting Machines (GBM)_Oversampling_...  
 111  Gradient Boosting Machines (GBM)_Oversampling_...  
 112  Gradient Boosting Machines (GBM)_Oversampling_...  
 113  Gradient Boosting Machines (GBM)_Oversampling_...  
 114  Gradient Boosting Machines (GBM)_Oversampling_...  
 115  Gradient Boosting Machines (GBM)_Oversampling_...  
 116  Gradient Boosting Machines (GBM)_Oversampling_...  
 117  Gradient Boosting Machines (GBM)_Oversampling_...  
 118  Gradient Boosting Machines (GBM)_Oversampling_...  
 119  Gradient Boosting Machines (GBM)_Oversampling_...  
 120  Gradient Boosting Machines (GBM)_Oversampling_...  
 121  Gradient Boosting Machines (GBM)_Oversampling_...  
 122  Gradient Boosting Machines (GBM)_Oversampling_...  
 123  Gradient Boosting Machines (GBM)_Oversampling_...  
 124  Gradient Boosting Machines (GBM)_Oversampling_...  
 125  Gradient Boosting Machines (GBM)_Oversampling_...  
 126  Gradient Boosting Machines (GBM)_Oversampling_...  
 127  Gradient Boosting Machines (GBM)_Oversampling_...  
 128  Gradient Boosting Machines (GBM)_Oversampling_...  
 129  Gradient Boosting Machines (GBM)_Oversampling_...  
 130  Gradient Boosting Machines (GBM)_Oversampling_...  
 131  Gradient Boosting Machines (GBM)_Oversampling_...  
 132  Gradient Boosting Machines (GBM)_Oversampling_...  
 133  Gradient Boosting Machines (GBM)_Oversampling_...  
 134  Gradient Boosting Machines (GBM)_Oversampling_...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.718725             NaN   
 1    Gradient Boosting Machines (GBM)  0.113488             NaN   
 2    Gradient Boosting Machines (GBM)  0.514768             NaN   
 3    Gradient Boosting Machines (GBM)  0.185976             NaN   
 4    Gradient Boosting Machines (GBM)  0.682874             NaN   
 5    Gradient Boosting Machines (GBM)  0.271569             NaN   
 6    Gradient Boosting Machines (GBM)  0.365748             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.706874             NaN   
 28   Gradient Boosting Machines (GBM)  0.084864             NaN   
 29   Gradient Boosting Machines (GBM)  0.591463             NaN   
 30   Gradient Boosting Machines (GBM)  0.148432             NaN   
 31   Gradient Boosting Machines (GBM)  0.708353             NaN   
 32   Gradient Boosting Machines (GBM)  0.335527             NaN   
 33   Gradient Boosting Machines (GBM)  0.416707             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.692389             NaN   
 55   Gradient Boosting Machines (GBM)  0.091590             NaN   
 56   Gradient Boosting Machines (GBM)  0.588235             NaN   
 57   Gradient Boosting Machines (GBM)  0.158501             NaN   
 58   Gradient Boosting Machines (GBM)  0.695575             NaN   
 59   Gradient Boosting Machines (GBM)  0.300278             NaN   
 60   Gradient Boosting Machines (GBM)  0.391151             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.703635             NaN   
 82   Gradient Boosting Machines (GBM)  0.095735             NaN   
 83   Gradient Boosting Machines (GBM)  0.561224             NaN   
 84   Gradient Boosting Machines (GBM)  0.163569             NaN   
 85   Gradient Boosting Machines (GBM)  0.691740             NaN   
 86   Gradient Boosting Machines (GBM)  0.301757             NaN   
 87   Gradient Boosting Machines (GBM)  0.383481             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.713383             NaN   
 109  Gradient Boosting Machines (GBM)  0.114841             NaN   
 110  Gradient Boosting Machines (GBM)  0.601852             NaN   
 111  Gradient Boosting Machines (GBM)  0.192878             NaN   
 112  Gradient Boosting Machines (GBM)  0.726379             NaN   
 113  Gradient Boosting Machines (GBM)  0.346638             NaN   
 114  Gradient Boosting Machines (GBM)  0.452757             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Mean Imputation              Oversampling   
 1        Mean Imputation              Oversampling   
 2        Mean Imputation              Oversampling   
 3        Mean Imputation              Oversampling   
 4        Mean Imputation              Oversampling   
 5        Mean Imputation              Oversampling   
 6        Mean Imputation              Oversampling   
 7        Mean Imputation              Oversampling   
 8        Mean Imputation              Oversampling   
 9        Mean Imputation              Oversampling   
 10       Mean Imputation              Oversampling   
 11       Mean Imputation              Oversampling   
 12       Mean Imputation              Oversampling   
 13       Mean Imputation              Oversampling   
 14       Mean Imputation              Oversampling   
 15       Mean Imputation              Oversampling   
 16       Mean Imputation              Oversampling   
 17       Mean Imputation              Oversampling   
 18       Mean Imputation              Oversampling   
 19       Mean Imputation              Oversampling   
 20       Mean Imputation              Oversampling   
 21       Mean Imputation              Oversampling   
 22       Mean Imputation              Oversampling   
 23       Mean Imputation              Oversampling   
 24       Mean Imputation              Oversampling   
 25       Mean Imputation              Oversampling   
 26       Mean Imputation              Oversampling   
 27       Mean Imputation              Oversampling   
 28       Mean Imputation              Oversampling   
 29       Mean Imputation              Oversampling   
 30       Mean Imputation              Oversampling   
 31       Mean Imputation              Oversampling   
 32       Mean Imputation              Oversampling   
 33       Mean Imputation              Oversampling   
 34       Mean Imputation              Oversampling   
 35       Mean Imputation              Oversampling   
 36       Mean Imputation              Oversampling   
 37       Mean Imputation              Oversampling   
 38       Mean Imputation              Oversampling   
 39       Mean Imputation              Oversampling   
 40       Mean Imputation              Oversampling   
 41       Mean Imputation              Oversampling   
 42       Mean Imputation              Oversampling   
 43       Mean Imputation              Oversampling   
 44       Mean Imputation              Oversampling   
 45       Mean Imputation              Oversampling   
 46       Mean Imputation              Oversampling   
 47       Mean Imputation              Oversampling   
 48       Mean Imputation              Oversampling   
 49       Mean Imputation              Oversampling   
 50       Mean Imputation              Oversampling   
 51       Mean Imputation              Oversampling   
 52       Mean Imputation              Oversampling   
 53       Mean Imputation              Oversampling   
 54       Mean Imputation              Oversampling   
 55       Mean Imputation              Oversampling   
 56       Mean Imputation              Oversampling   
 57       Mean Imputation              Oversampling   
 58       Mean Imputation              Oversampling   
 59       Mean Imputation              Oversampling   
 60       Mean Imputation              Oversampling   
 61       Mean Imputation              Oversampling   
 62       Mean Imputation              Oversampling   
 63       Mean Imputation              Oversampling   
 64       Mean Imputation              Oversampling   
 65       Mean Imputation              Oversampling   
 66       Mean Imputation              Oversampling   
 67       Mean Imputation              Oversampling   
 68       Mean Imputation              Oversampling   
 69       Mean Imputation              Oversampling   
 70       Mean Imputation              Oversampling   
 71       Mean Imputation              Oversampling   
 72       Mean Imputation              Oversampling   
 73       Mean Imputation              Oversampling   
 74       Mean Imputation              Oversampling   
 75       Mean Imputation              Oversampling   
 76       Mean Imputation              Oversampling   
 77       Mean Imputation              Oversampling   
 78       Mean Imputation              Oversampling   
 79       Mean Imputation              Oversampling   
 80       Mean Imputation              Oversampling   
 81       Mean Imputation              Oversampling   
 82       Mean Imputation              Oversampling   
 83       Mean Imputation              Oversampling   
 84       Mean Imputation              Oversampling   
 85       Mean Imputation              Oversampling   
 86       Mean Imputation              Oversampling   
 87       Mean Imputation              Oversampling   
 88       Mean Imputation              Oversampling   
 89       Mean Imputation              Oversampling   
 90       Mean Imputation              Oversampling   
 91       Mean Imputation              Oversampling   
 92       Mean Imputation              Oversampling   
 93       Mean Imputation              Oversampling   
 94       Mean Imputation              Oversampling   
 95       Mean Imputation              Oversampling   
 96       Mean Imputation              Oversampling   
 97       Mean Imputation              Oversampling   
 98       Mean Imputation              Oversampling   
 99       Mean Imputation              Oversampling   
 100      Mean Imputation              Oversampling   
 101      Mean Imputation              Oversampling   
 102      Mean Imputation              Oversampling   
 103      Mean Imputation              Oversampling   
 104      Mean Imputation              Oversampling   
 105      Mean Imputation              Oversampling   
 106      Mean Imputation              Oversampling   
 107      Mean Imputation              Oversampling   
 108      Mean Imputation              Oversampling   
 109      Mean Imputation              Oversampling   
 110      Mean Imputation              Oversampling   
 111      Mean Imputation              Oversampling   
 112      Mean Imputation              Oversampling   
 113      Mean Imputation              Oversampling   
 114      Mean Imputation              Oversampling   
 115      Mean Imputation              Oversampling   
 116      Mean Imputation              Oversampling   
 117      Mean Imputation              Oversampling   
 118      Mean Imputation              Oversampling   
 119      Mean Imputation              Oversampling   
 120      Mean Imputation              Oversampling   
 121      Mean Imputation              Oversampling   
 122      Mean Imputation              Oversampling   
 123      Mean Imputation              Oversampling   
 124      Mean Imputation              Oversampling   
 125      Mean Imputation              Oversampling   
 126      Mean Imputation              Oversampling   
 127      Mean Imputation              Oversampling   
 128      Mean Imputation              Oversampling   
 129      Mean Imputation              Oversampling   
 130      Mean Imputation              Oversampling   
 131      Mean Imputation              Oversampling   
 132      Mean Imputation              Oversampling   
 133      Mean Imputation              Oversampling   
 134      Mean Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Oversampling_...  
 1    Gradient Boosting Machines (GBM)_Oversampling_...  
 2    Gradient Boosting Machines (GBM)_Oversampling_...  
 3    Gradient Boosting Machines (GBM)_Oversampling_...  
 4    Gradient Boosting Machines (GBM)_Oversampling_...  
 5    Gradient Boosting Machines (GBM)_Oversampling_...  
 6    Gradient Boosting Machines (GBM)_Oversampling_...  
 7    Gradient Boosting Machines (GBM)_Oversampling_...  
 8    Gradient Boosting Machines (GBM)_Oversampling_...  
 9    Gradient Boosting Machines (GBM)_Oversampling_...  
 10   Gradient Boosting Machines (GBM)_Oversampling_...  
 11   Gradient Boosting Machines (GBM)_Oversampling_...  
 12   Gradient Boosting Machines (GBM)_Oversampling_...  
 13   Gradient Boosting Machines (GBM)_Oversampling_...  
 14   Gradient Boosting Machines (GBM)_Oversampling_...  
 15   Gradient Boosting Machines (GBM)_Oversampling_...  
 16   Gradient Boosting Machines (GBM)_Oversampling_...  
 17   Gradient Boosting Machines (GBM)_Oversampling_...  
 18   Gradient Boosting Machines (GBM)_Oversampling_...  
 19   Gradient Boosting Machines (GBM)_Oversampling_...  
 20   Gradient Boosting Machines (GBM)_Oversampling_...  
 21   Gradient Boosting Machines (GBM)_Oversampling_...  
 22   Gradient Boosting Machines (GBM)_Oversampling_...  
 23   Gradient Boosting Machines (GBM)_Oversampling_...  
 24   Gradient Boosting Machines (GBM)_Oversampling_...  
 25   Gradient Boosting Machines (GBM)_Oversampling_...  
 26   Gradient Boosting Machines (GBM)_Oversampling_...  
 27   Gradient Boosting Machines (GBM)_Oversampling_...  
 28   Gradient Boosting Machines (GBM)_Oversampling_...  
 29   Gradient Boosting Machines (GBM)_Oversampling_...  
 30   Gradient Boosting Machines (GBM)_Oversampling_...  
 31   Gradient Boosting Machines (GBM)_Oversampling_...  
 32   Gradient Boosting Machines (GBM)_Oversampling_...  
 33   Gradient Boosting Machines (GBM)_Oversampling_...  
 34   Gradient Boosting Machines (GBM)_Oversampling_...  
 35   Gradient Boosting Machines (GBM)_Oversampling_...  
 36   Gradient Boosting Machines (GBM)_Oversampling_...  
 37   Gradient Boosting Machines (GBM)_Oversampling_...  
 38   Gradient Boosting Machines (GBM)_Oversampling_...  
 39   Gradient Boosting Machines (GBM)_Oversampling_...  
 40   Gradient Boosting Machines (GBM)_Oversampling_...  
 41   Gradient Boosting Machines (GBM)_Oversampling_...  
 42   Gradient Boosting Machines (GBM)_Oversampling_...  
 43   Gradient Boosting Machines (GBM)_Oversampling_...  
 44   Gradient Boosting Machines (GBM)_Oversampling_...  
 45   Gradient Boosting Machines (GBM)_Oversampling_...  
 46   Gradient Boosting Machines (GBM)_Oversampling_...  
 47   Gradient Boosting Machines (GBM)_Oversampling_...  
 48   Gradient Boosting Machines (GBM)_Oversampling_...  
 49   Gradient Boosting Machines (GBM)_Oversampling_...  
 50   Gradient Boosting Machines (GBM)_Oversampling_...  
 51   Gradient Boosting Machines (GBM)_Oversampling_...  
 52   Gradient Boosting Machines (GBM)_Oversampling_...  
 53   Gradient Boosting Machines (GBM)_Oversampling_...  
 54   Gradient Boosting Machines (GBM)_Oversampling_...  
 55   Gradient Boosting Machines (GBM)_Oversampling_...  
 56   Gradient Boosting Machines (GBM)_Oversampling_...  
 57   Gradient Boosting Machines (GBM)_Oversampling_...  
 58   Gradient Boosting Machines (GBM)_Oversampling_...  
 59   Gradient Boosting Machines (GBM)_Oversampling_...  
 60   Gradient Boosting Machines (GBM)_Oversampling_...  
 61   Gradient Boosting Machines (GBM)_Oversampling_...  
 62   Gradient Boosting Machines (GBM)_Oversampling_...  
 63   Gradient Boosting Machines (GBM)_Oversampling_...  
 64   Gradient Boosting Machines (GBM)_Oversampling_...  
 65   Gradient Boosting Machines (GBM)_Oversampling_...  
 66   Gradient Boosting Machines (GBM)_Oversampling_...  
 67   Gradient Boosting Machines (GBM)_Oversampling_...  
 68   Gradient Boosting Machines (GBM)_Oversampling_...  
 69   Gradient Boosting Machines (GBM)_Oversampling_...  
 70   Gradient Boosting Machines (GBM)_Oversampling_...  
 71   Gradient Boosting Machines (GBM)_Oversampling_...  
 72   Gradient Boosting Machines (GBM)_Oversampling_...  
 73   Gradient Boosting Machines (GBM)_Oversampling_...  
 74   Gradient Boosting Machines (GBM)_Oversampling_...  
 75   Gradient Boosting Machines (GBM)_Oversampling_...  
 76   Gradient Boosting Machines (GBM)_Oversampling_...  
 77   Gradient Boosting Machines (GBM)_Oversampling_...  
 78   Gradient Boosting Machines (GBM)_Oversampling_...  
 79   Gradient Boosting Machines (GBM)_Oversampling_...  
 80   Gradient Boosting Machines (GBM)_Oversampling_...  
 81   Gradient Boosting Machines (GBM)_Oversampling_...  
 82   Gradient Boosting Machines (GBM)_Oversampling_...  
 83   Gradient Boosting Machines (GBM)_Oversampling_...  
 84   Gradient Boosting Machines (GBM)_Oversampling_...  
 85   Gradient Boosting Machines (GBM)_Oversampling_...  
 86   Gradient Boosting Machines (GBM)_Oversampling_...  
 87   Gradient Boosting Machines (GBM)_Oversampling_...  
 88   Gradient Boosting Machines (GBM)_Oversampling_...  
 89   Gradient Boosting Machines (GBM)_Oversampling_...  
 90   Gradient Boosting Machines (GBM)_Oversampling_...  
 91   Gradient Boosting Machines (GBM)_Oversampling_...  
 92   Gradient Boosting Machines (GBM)_Oversampling_...  
 93   Gradient Boosting Machines (GBM)_Oversampling_...  
 94   Gradient Boosting Machines (GBM)_Oversampling_...  
 95   Gradient Boosting Machines (GBM)_Oversampling_...  
 96   Gradient Boosting Machines (GBM)_Oversampling_...  
 97   Gradient Boosting Machines (GBM)_Oversampling_...  
 98   Gradient Boosting Machines (GBM)_Oversampling_...  
 99   Gradient Boosting Machines (GBM)_Oversampling_...  
 100  Gradient Boosting Machines (GBM)_Oversampling_...  
 101  Gradient Boosting Machines (GBM)_Oversampling_...  
 102  Gradient Boosting Machines (GBM)_Oversampling_...  
 103  Gradient Boosting Machines (GBM)_Oversampling_...  
 104  Gradient Boosting Machines (GBM)_Oversampling_...  
 105  Gradient Boosting Machines (GBM)_Oversampling_...  
 106  Gradient Boosting Machines (GBM)_Oversampling_...  
 107  Gradient Boosting Machines (GBM)_Oversampling_...  
 108  Gradient Boosting Machines (GBM)_Oversampling_...  
 109  Gradient Boosting Machines (GBM)_Oversampling_...  
 110  Gradient Boosting Machines (GBM)_Oversampling_...  
 111  Gradient Boosting Machines (GBM)_Oversampling_...  
 112  Gradient Boosting Machines (GBM)_Oversampling_...  
 113  Gradient Boosting Machines (GBM)_Oversampling_...  
 114  Gradient Boosting Machines (GBM)_Oversampling_...  
 115  Gradient Boosting Machines (GBM)_Oversampling_...  
 116  Gradient Boosting Machines (GBM)_Oversampling_...  
 117  Gradient Boosting Machines (GBM)_Oversampling_...  
 118  Gradient Boosting Machines (GBM)_Oversampling_...  
 119  Gradient Boosting Machines (GBM)_Oversampling_...  
 120  Gradient Boosting Machines (GBM)_Oversampling_...  
 121  Gradient Boosting Machines (GBM)_Oversampling_...  
 122  Gradient Boosting Machines (GBM)_Oversampling_...  
 123  Gradient Boosting Machines (GBM)_Oversampling_...  
 124  Gradient Boosting Machines (GBM)_Oversampling_...  
 125  Gradient Boosting Machines (GBM)_Oversampling_...  
 126  Gradient Boosting Machines (GBM)_Oversampling_...  
 127  Gradient Boosting Machines (GBM)_Oversampling_...  
 128  Gradient Boosting Machines (GBM)_Oversampling_...  
 129  Gradient Boosting Machines (GBM)_Oversampling_...  
 130  Gradient Boosting Machines (GBM)_Oversampling_...  
 131  Gradient Boosting Machines (GBM)_Oversampling_...  
 132  Gradient Boosting Machines (GBM)_Oversampling_...  
 133  Gradient Boosting Machines (GBM)_Oversampling_...  
 134  Gradient Boosting Machines (GBM)_Oversampling_...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.703713             NaN   
 1    Gradient Boosting Machines (GBM)  0.117241             NaN   
 2    Gradient Boosting Machines (GBM)  0.573840             NaN   
 3    Gradient Boosting Machines (GBM)  0.194703             NaN   
 4    Gradient Boosting Machines (GBM)  0.699783             NaN   
 5    Gradient Boosting Machines (GBM)  0.301492             NaN   
 6    Gradient Boosting Machines (GBM)  0.399565             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.695286             NaN   
 28   Gradient Boosting Machines (GBM)  0.080304             NaN   
 29   Gradient Boosting Machines (GBM)  0.579268             NaN   
 30   Gradient Boosting Machines (GBM)  0.141054             NaN   
 31   Gradient Boosting Machines (GBM)  0.706890             NaN   
 32   Gradient Boosting Machines (GBM)  0.339134             NaN   
 33   Gradient Boosting Machines (GBM)  0.413780             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.690809             NaN   
 55   Gradient Boosting Machines (GBM)  0.094495             NaN   
 56   Gradient Boosting Machines (GBM)  0.614973             NaN   
 57   Gradient Boosting Machines (GBM)  0.163818             NaN   
 58   Gradient Boosting Machines (GBM)  0.699653             NaN   
 59   Gradient Boosting Machines (GBM)  0.320946             NaN   
 60   Gradient Boosting Machines (GBM)  0.399307             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.688356             NaN   
 82   Gradient Boosting Machines (GBM)  0.097800             NaN   
 83   Gradient Boosting Machines (GBM)  0.612245             NaN   
 84   Gradient Boosting Machines (GBM)  0.168658             NaN   
 85   Gradient Boosting Machines (GBM)  0.703859             NaN   
 86   Gradient Boosting Machines (GBM)  0.320193             NaN   
 87   Gradient Boosting Machines (GBM)  0.407718             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.720232             NaN   
 109  Gradient Boosting Machines (GBM)  0.115455             NaN   
 110  Gradient Boosting Machines (GBM)  0.587963             NaN   
 111  Gradient Boosting Machines (GBM)  0.193009             NaN   
 112  Gradient Boosting Machines (GBM)  0.709960             NaN   
 113  Gradient Boosting Machines (GBM)  0.328073             NaN   
 114  Gradient Boosting Machines (GBM)  0.419919             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0      Median Imputation              Oversampling   
 1      Median Imputation              Oversampling   
 2      Median Imputation              Oversampling   
 3      Median Imputation              Oversampling   
 4      Median Imputation              Oversampling   
 5      Median Imputation              Oversampling   
 6      Median Imputation              Oversampling   
 7      Median Imputation              Oversampling   
 8      Median Imputation              Oversampling   
 9      Median Imputation              Oversampling   
 10     Median Imputation              Oversampling   
 11     Median Imputation              Oversampling   
 12     Median Imputation              Oversampling   
 13     Median Imputation              Oversampling   
 14     Median Imputation              Oversampling   
 15     Median Imputation              Oversampling   
 16     Median Imputation              Oversampling   
 17     Median Imputation              Oversampling   
 18     Median Imputation              Oversampling   
 19     Median Imputation              Oversampling   
 20     Median Imputation              Oversampling   
 21     Median Imputation              Oversampling   
 22     Median Imputation              Oversampling   
 23     Median Imputation              Oversampling   
 24     Median Imputation              Oversampling   
 25     Median Imputation              Oversampling   
 26     Median Imputation              Oversampling   
 27     Median Imputation              Oversampling   
 28     Median Imputation              Oversampling   
 29     Median Imputation              Oversampling   
 30     Median Imputation              Oversampling   
 31     Median Imputation              Oversampling   
 32     Median Imputation              Oversampling   
 33     Median Imputation              Oversampling   
 34     Median Imputation              Oversampling   
 35     Median Imputation              Oversampling   
 36     Median Imputation              Oversampling   
 37     Median Imputation              Oversampling   
 38     Median Imputation              Oversampling   
 39     Median Imputation              Oversampling   
 40     Median Imputation              Oversampling   
 41     Median Imputation              Oversampling   
 42     Median Imputation              Oversampling   
 43     Median Imputation              Oversampling   
 44     Median Imputation              Oversampling   
 45     Median Imputation              Oversampling   
 46     Median Imputation              Oversampling   
 47     Median Imputation              Oversampling   
 48     Median Imputation              Oversampling   
 49     Median Imputation              Oversampling   
 50     Median Imputation              Oversampling   
 51     Median Imputation              Oversampling   
 52     Median Imputation              Oversampling   
 53     Median Imputation              Oversampling   
 54     Median Imputation              Oversampling   
 55     Median Imputation              Oversampling   
 56     Median Imputation              Oversampling   
 57     Median Imputation              Oversampling   
 58     Median Imputation              Oversampling   
 59     Median Imputation              Oversampling   
 60     Median Imputation              Oversampling   
 61     Median Imputation              Oversampling   
 62     Median Imputation              Oversampling   
 63     Median Imputation              Oversampling   
 64     Median Imputation              Oversampling   
 65     Median Imputation              Oversampling   
 66     Median Imputation              Oversampling   
 67     Median Imputation              Oversampling   
 68     Median Imputation              Oversampling   
 69     Median Imputation              Oversampling   
 70     Median Imputation              Oversampling   
 71     Median Imputation              Oversampling   
 72     Median Imputation              Oversampling   
 73     Median Imputation              Oversampling   
 74     Median Imputation              Oversampling   
 75     Median Imputation              Oversampling   
 76     Median Imputation              Oversampling   
 77     Median Imputation              Oversampling   
 78     Median Imputation              Oversampling   
 79     Median Imputation              Oversampling   
 80     Median Imputation              Oversampling   
 81     Median Imputation              Oversampling   
 82     Median Imputation              Oversampling   
 83     Median Imputation              Oversampling   
 84     Median Imputation              Oversampling   
 85     Median Imputation              Oversampling   
 86     Median Imputation              Oversampling   
 87     Median Imputation              Oversampling   
 88     Median Imputation              Oversampling   
 89     Median Imputation              Oversampling   
 90     Median Imputation              Oversampling   
 91     Median Imputation              Oversampling   
 92     Median Imputation              Oversampling   
 93     Median Imputation              Oversampling   
 94     Median Imputation              Oversampling   
 95     Median Imputation              Oversampling   
 96     Median Imputation              Oversampling   
 97     Median Imputation              Oversampling   
 98     Median Imputation              Oversampling   
 99     Median Imputation              Oversampling   
 100    Median Imputation              Oversampling   
 101    Median Imputation              Oversampling   
 102    Median Imputation              Oversampling   
 103    Median Imputation              Oversampling   
 104    Median Imputation              Oversampling   
 105    Median Imputation              Oversampling   
 106    Median Imputation              Oversampling   
 107    Median Imputation              Oversampling   
 108    Median Imputation              Oversampling   
 109    Median Imputation              Oversampling   
 110    Median Imputation              Oversampling   
 111    Median Imputation              Oversampling   
 112    Median Imputation              Oversampling   
 113    Median Imputation              Oversampling   
 114    Median Imputation              Oversampling   
 115    Median Imputation              Oversampling   
 116    Median Imputation              Oversampling   
 117    Median Imputation              Oversampling   
 118    Median Imputation              Oversampling   
 119    Median Imputation              Oversampling   
 120    Median Imputation              Oversampling   
 121    Median Imputation              Oversampling   
 122    Median Imputation              Oversampling   
 123    Median Imputation              Oversampling   
 124    Median Imputation              Oversampling   
 125    Median Imputation              Oversampling   
 126    Median Imputation              Oversampling   
 127    Median Imputation              Oversampling   
 128    Median Imputation              Oversampling   
 129    Median Imputation              Oversampling   
 130    Median Imputation              Oversampling   
 131    Median Imputation              Oversampling   
 132    Median Imputation              Oversampling   
 133    Median Imputation              Oversampling   
 134    Median Imputation              Oversampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Oversampling_...  
 1    Gradient Boosting Machines (GBM)_Oversampling_...  
 2    Gradient Boosting Machines (GBM)_Oversampling_...  
 3    Gradient Boosting Machines (GBM)_Oversampling_...  
 4    Gradient Boosting Machines (GBM)_Oversampling_...  
 5    Gradient Boosting Machines (GBM)_Oversampling_...  
 6    Gradient Boosting Machines (GBM)_Oversampling_...  
 7    Gradient Boosting Machines (GBM)_Oversampling_...  
 8    Gradient Boosting Machines (GBM)_Oversampling_...  
 9    Gradient Boosting Machines (GBM)_Oversampling_...  
 10   Gradient Boosting Machines (GBM)_Oversampling_...  
 11   Gradient Boosting Machines (GBM)_Oversampling_...  
 12   Gradient Boosting Machines (GBM)_Oversampling_...  
 13   Gradient Boosting Machines (GBM)_Oversampling_...  
 14   Gradient Boosting Machines (GBM)_Oversampling_...  
 15   Gradient Boosting Machines (GBM)_Oversampling_...  
 16   Gradient Boosting Machines (GBM)_Oversampling_...  
 17   Gradient Boosting Machines (GBM)_Oversampling_...  
 18   Gradient Boosting Machines (GBM)_Oversampling_...  
 19   Gradient Boosting Machines (GBM)_Oversampling_...  
 20   Gradient Boosting Machines (GBM)_Oversampling_...  
 21   Gradient Boosting Machines (GBM)_Oversampling_...  
 22   Gradient Boosting Machines (GBM)_Oversampling_...  
 23   Gradient Boosting Machines (GBM)_Oversampling_...  
 24   Gradient Boosting Machines (GBM)_Oversampling_...  
 25   Gradient Boosting Machines (GBM)_Oversampling_...  
 26   Gradient Boosting Machines (GBM)_Oversampling_...  
 27   Gradient Boosting Machines (GBM)_Oversampling_...  
 28   Gradient Boosting Machines (GBM)_Oversampling_...  
 29   Gradient Boosting Machines (GBM)_Oversampling_...  
 30   Gradient Boosting Machines (GBM)_Oversampling_...  
 31   Gradient Boosting Machines (GBM)_Oversampling_...  
 32   Gradient Boosting Machines (GBM)_Oversampling_...  
 33   Gradient Boosting Machines (GBM)_Oversampling_...  
 34   Gradient Boosting Machines (GBM)_Oversampling_...  
 35   Gradient Boosting Machines (GBM)_Oversampling_...  
 36   Gradient Boosting Machines (GBM)_Oversampling_...  
 37   Gradient Boosting Machines (GBM)_Oversampling_...  
 38   Gradient Boosting Machines (GBM)_Oversampling_...  
 39   Gradient Boosting Machines (GBM)_Oversampling_...  
 40   Gradient Boosting Machines (GBM)_Oversampling_...  
 41   Gradient Boosting Machines (GBM)_Oversampling_...  
 42   Gradient Boosting Machines (GBM)_Oversampling_...  
 43   Gradient Boosting Machines (GBM)_Oversampling_...  
 44   Gradient Boosting Machines (GBM)_Oversampling_...  
 45   Gradient Boosting Machines (GBM)_Oversampling_...  
 46   Gradient Boosting Machines (GBM)_Oversampling_...  
 47   Gradient Boosting Machines (GBM)_Oversampling_...  
 48   Gradient Boosting Machines (GBM)_Oversampling_...  
 49   Gradient Boosting Machines (GBM)_Oversampling_...  
 50   Gradient Boosting Machines (GBM)_Oversampling_...  
 51   Gradient Boosting Machines (GBM)_Oversampling_...  
 52   Gradient Boosting Machines (GBM)_Oversampling_...  
 53   Gradient Boosting Machines (GBM)_Oversampling_...  
 54   Gradient Boosting Machines (GBM)_Oversampling_...  
 55   Gradient Boosting Machines (GBM)_Oversampling_...  
 56   Gradient Boosting Machines (GBM)_Oversampling_...  
 57   Gradient Boosting Machines (GBM)_Oversampling_...  
 58   Gradient Boosting Machines (GBM)_Oversampling_...  
 59   Gradient Boosting Machines (GBM)_Oversampling_...  
 60   Gradient Boosting Machines (GBM)_Oversampling_...  
 61   Gradient Boosting Machines (GBM)_Oversampling_...  
 62   Gradient Boosting Machines (GBM)_Oversampling_...  
 63   Gradient Boosting Machines (GBM)_Oversampling_...  
 64   Gradient Boosting Machines (GBM)_Oversampling_...  
 65   Gradient Boosting Machines (GBM)_Oversampling_...  
 66   Gradient Boosting Machines (GBM)_Oversampling_...  
 67   Gradient Boosting Machines (GBM)_Oversampling_...  
 68   Gradient Boosting Machines (GBM)_Oversampling_...  
 69   Gradient Boosting Machines (GBM)_Oversampling_...  
 70   Gradient Boosting Machines (GBM)_Oversampling_...  
 71   Gradient Boosting Machines (GBM)_Oversampling_...  
 72   Gradient Boosting Machines (GBM)_Oversampling_...  
 73   Gradient Boosting Machines (GBM)_Oversampling_...  
 74   Gradient Boosting Machines (GBM)_Oversampling_...  
 75   Gradient Boosting Machines (GBM)_Oversampling_...  
 76   Gradient Boosting Machines (GBM)_Oversampling_...  
 77   Gradient Boosting Machines (GBM)_Oversampling_...  
 78   Gradient Boosting Machines (GBM)_Oversampling_...  
 79   Gradient Boosting Machines (GBM)_Oversampling_...  
 80   Gradient Boosting Machines (GBM)_Oversampling_...  
 81   Gradient Boosting Machines (GBM)_Oversampling_...  
 82   Gradient Boosting Machines (GBM)_Oversampling_...  
 83   Gradient Boosting Machines (GBM)_Oversampling_...  
 84   Gradient Boosting Machines (GBM)_Oversampling_...  
 85   Gradient Boosting Machines (GBM)_Oversampling_...  
 86   Gradient Boosting Machines (GBM)_Oversampling_...  
 87   Gradient Boosting Machines (GBM)_Oversampling_...  
 88   Gradient Boosting Machines (GBM)_Oversampling_...  
 89   Gradient Boosting Machines (GBM)_Oversampling_...  
 90   Gradient Boosting Machines (GBM)_Oversampling_...  
 91   Gradient Boosting Machines (GBM)_Oversampling_...  
 92   Gradient Boosting Machines (GBM)_Oversampling_...  
 93   Gradient Boosting Machines (GBM)_Oversampling_...  
 94   Gradient Boosting Machines (GBM)_Oversampling_...  
 95   Gradient Boosting Machines (GBM)_Oversampling_...  
 96   Gradient Boosting Machines (GBM)_Oversampling_...  
 97   Gradient Boosting Machines (GBM)_Oversampling_...  
 98   Gradient Boosting Machines (GBM)_Oversampling_...  
 99   Gradient Boosting Machines (GBM)_Oversampling_...  
 100  Gradient Boosting Machines (GBM)_Oversampling_...  
 101  Gradient Boosting Machines (GBM)_Oversampling_...  
 102  Gradient Boosting Machines (GBM)_Oversampling_...  
 103  Gradient Boosting Machines (GBM)_Oversampling_...  
 104  Gradient Boosting Machines (GBM)_Oversampling_...  
 105  Gradient Boosting Machines (GBM)_Oversampling_...  
 106  Gradient Boosting Machines (GBM)_Oversampling_...  
 107  Gradient Boosting Machines (GBM)_Oversampling_...  
 108  Gradient Boosting Machines (GBM)_Oversampling_...  
 109  Gradient Boosting Machines (GBM)_Oversampling_...  
 110  Gradient Boosting Machines (GBM)_Oversampling_...  
 111  Gradient Boosting Machines (GBM)_Oversampling_...  
 112  Gradient Boosting Machines (GBM)_Oversampling_...  
 113  Gradient Boosting Machines (GBM)_Oversampling_...  
 114  Gradient Boosting Machines (GBM)_Oversampling_...  
 115  Gradient Boosting Machines (GBM)_Oversampling_...  
 116  Gradient Boosting Machines (GBM)_Oversampling_...  
 117  Gradient Boosting Machines (GBM)_Oversampling_...  
 118  Gradient Boosting Machines (GBM)_Oversampling_...  
 119  Gradient Boosting Machines (GBM)_Oversampling_...  
 120  Gradient Boosting Machines (GBM)_Oversampling_...  
 121  Gradient Boosting Machines (GBM)_Oversampling_...  
 122  Gradient Boosting Machines (GBM)_Oversampling_...  
 123  Gradient Boosting Machines (GBM)_Oversampling_...  
 124  Gradient Boosting Machines (GBM)_Oversampling_...  
 125  Gradient Boosting Machines (GBM)_Oversampling_...  
 126  Gradient Boosting Machines (GBM)_Oversampling_...  
 127  Gradient Boosting Machines (GBM)_Oversampling_...  
 128  Gradient Boosting Machines (GBM)_Oversampling_...  
 129  Gradient Boosting Machines (GBM)_Oversampling_...  
 130  Gradient Boosting Machines (GBM)_Oversampling_...  
 131  Gradient Boosting Machines (GBM)_Oversampling_...  
 132  Gradient Boosting Machines (GBM)_Oversampling_...  
 133  Gradient Boosting Machines (GBM)_Oversampling_...  
 134  Gradient Boosting Machines (GBM)_Oversampling_...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.624967             NaN   
 1    Gradient Boosting Machines (GBM)  0.106171             NaN   
 2    Gradient Boosting Machines (GBM)  0.675105             NaN   
 3    Gradient Boosting Machines (GBM)  0.183486             NaN   
 4    Gradient Boosting Machines (GBM)  0.691533             NaN   
 5    Gradient Boosting Machines (GBM)  0.305490             NaN   
 6    Gradient Boosting Machines (GBM)  0.383067             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.640769             NaN   
 28   Gradient Boosting Machines (GBM)  0.079832             NaN   
 29   Gradient Boosting Machines (GBM)  0.695122             NaN   
 30   Gradient Boosting Machines (GBM)  0.143216             NaN   
 31   Gradient Boosting Machines (GBM)  0.707087             NaN   
 32   Gradient Boosting Machines (GBM)  0.339493             NaN   
 33   Gradient Boosting Machines (GBM)  0.414174             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.608638             NaN   
 55   Gradient Boosting Machines (GBM)  0.076321             NaN   
 56   Gradient Boosting Machines (GBM)  0.625668             NaN   
 57   Gradient Boosting Machines (GBM)  0.136047             NaN   
 58   Gradient Boosting Machines (GBM)  0.672355             NaN   
 59   Gradient Boosting Machines (GBM)  0.249623             NaN   
 60   Gradient Boosting Machines (GBM)  0.344709             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.619863             NaN   
 82   Gradient Boosting Machines (GBM)  0.082384             NaN   
 83   Gradient Boosting Machines (GBM)  0.627551             NaN   
 84   Gradient Boosting Machines (GBM)  0.145648             NaN   
 85   Gradient Boosting Machines (GBM)  0.674713             NaN   
 86   Gradient Boosting Machines (GBM)  0.272523             NaN   
 87   Gradient Boosting Machines (GBM)  0.349426             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.610906             NaN   
 109  Gradient Boosting Machines (GBM)  0.098663             NaN   
 110  Gradient Boosting Machines (GBM)  0.717593             NaN   
 111  Gradient Boosting Machines (GBM)  0.173475             NaN   
 112  Gradient Boosting Machines (GBM)  0.710280             NaN   
 113  Gradient Boosting Machines (GBM)  0.336489             NaN   
 114  Gradient Boosting Machines (GBM)  0.420559             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Zero Imputation             Undersampling   
 1        Zero Imputation             Undersampling   
 2        Zero Imputation             Undersampling   
 3        Zero Imputation             Undersampling   
 4        Zero Imputation             Undersampling   
 5        Zero Imputation             Undersampling   
 6        Zero Imputation             Undersampling   
 7        Zero Imputation             Undersampling   
 8        Zero Imputation             Undersampling   
 9        Zero Imputation             Undersampling   
 10       Zero Imputation             Undersampling   
 11       Zero Imputation             Undersampling   
 12       Zero Imputation             Undersampling   
 13       Zero Imputation             Undersampling   
 14       Zero Imputation             Undersampling   
 15       Zero Imputation             Undersampling   
 16       Zero Imputation             Undersampling   
 17       Zero Imputation             Undersampling   
 18       Zero Imputation             Undersampling   
 19       Zero Imputation             Undersampling   
 20       Zero Imputation             Undersampling   
 21       Zero Imputation             Undersampling   
 22       Zero Imputation             Undersampling   
 23       Zero Imputation             Undersampling   
 24       Zero Imputation             Undersampling   
 25       Zero Imputation             Undersampling   
 26       Zero Imputation             Undersampling   
 27       Zero Imputation             Undersampling   
 28       Zero Imputation             Undersampling   
 29       Zero Imputation             Undersampling   
 30       Zero Imputation             Undersampling   
 31       Zero Imputation             Undersampling   
 32       Zero Imputation             Undersampling   
 33       Zero Imputation             Undersampling   
 34       Zero Imputation             Undersampling   
 35       Zero Imputation             Undersampling   
 36       Zero Imputation             Undersampling   
 37       Zero Imputation             Undersampling   
 38       Zero Imputation             Undersampling   
 39       Zero Imputation             Undersampling   
 40       Zero Imputation             Undersampling   
 41       Zero Imputation             Undersampling   
 42       Zero Imputation             Undersampling   
 43       Zero Imputation             Undersampling   
 44       Zero Imputation             Undersampling   
 45       Zero Imputation             Undersampling   
 46       Zero Imputation             Undersampling   
 47       Zero Imputation             Undersampling   
 48       Zero Imputation             Undersampling   
 49       Zero Imputation             Undersampling   
 50       Zero Imputation             Undersampling   
 51       Zero Imputation             Undersampling   
 52       Zero Imputation             Undersampling   
 53       Zero Imputation             Undersampling   
 54       Zero Imputation             Undersampling   
 55       Zero Imputation             Undersampling   
 56       Zero Imputation             Undersampling   
 57       Zero Imputation             Undersampling   
 58       Zero Imputation             Undersampling   
 59       Zero Imputation             Undersampling   
 60       Zero Imputation             Undersampling   
 61       Zero Imputation             Undersampling   
 62       Zero Imputation             Undersampling   
 63       Zero Imputation             Undersampling   
 64       Zero Imputation             Undersampling   
 65       Zero Imputation             Undersampling   
 66       Zero Imputation             Undersampling   
 67       Zero Imputation             Undersampling   
 68       Zero Imputation             Undersampling   
 69       Zero Imputation             Undersampling   
 70       Zero Imputation             Undersampling   
 71       Zero Imputation             Undersampling   
 72       Zero Imputation             Undersampling   
 73       Zero Imputation             Undersampling   
 74       Zero Imputation             Undersampling   
 75       Zero Imputation             Undersampling   
 76       Zero Imputation             Undersampling   
 77       Zero Imputation             Undersampling   
 78       Zero Imputation             Undersampling   
 79       Zero Imputation             Undersampling   
 80       Zero Imputation             Undersampling   
 81       Zero Imputation             Undersampling   
 82       Zero Imputation             Undersampling   
 83       Zero Imputation             Undersampling   
 84       Zero Imputation             Undersampling   
 85       Zero Imputation             Undersampling   
 86       Zero Imputation             Undersampling   
 87       Zero Imputation             Undersampling   
 88       Zero Imputation             Undersampling   
 89       Zero Imputation             Undersampling   
 90       Zero Imputation             Undersampling   
 91       Zero Imputation             Undersampling   
 92       Zero Imputation             Undersampling   
 93       Zero Imputation             Undersampling   
 94       Zero Imputation             Undersampling   
 95       Zero Imputation             Undersampling   
 96       Zero Imputation             Undersampling   
 97       Zero Imputation             Undersampling   
 98       Zero Imputation             Undersampling   
 99       Zero Imputation             Undersampling   
 100      Zero Imputation             Undersampling   
 101      Zero Imputation             Undersampling   
 102      Zero Imputation             Undersampling   
 103      Zero Imputation             Undersampling   
 104      Zero Imputation             Undersampling   
 105      Zero Imputation             Undersampling   
 106      Zero Imputation             Undersampling   
 107      Zero Imputation             Undersampling   
 108      Zero Imputation             Undersampling   
 109      Zero Imputation             Undersampling   
 110      Zero Imputation             Undersampling   
 111      Zero Imputation             Undersampling   
 112      Zero Imputation             Undersampling   
 113      Zero Imputation             Undersampling   
 114      Zero Imputation             Undersampling   
 115      Zero Imputation             Undersampling   
 116      Zero Imputation             Undersampling   
 117      Zero Imputation             Undersampling   
 118      Zero Imputation             Undersampling   
 119      Zero Imputation             Undersampling   
 120      Zero Imputation             Undersampling   
 121      Zero Imputation             Undersampling   
 122      Zero Imputation             Undersampling   
 123      Zero Imputation             Undersampling   
 124      Zero Imputation             Undersampling   
 125      Zero Imputation             Undersampling   
 126      Zero Imputation             Undersampling   
 127      Zero Imputation             Undersampling   
 128      Zero Imputation             Undersampling   
 129      Zero Imputation             Undersampling   
 130      Zero Imputation             Undersampling   
 131      Zero Imputation             Undersampling   
 132      Zero Imputation             Undersampling   
 133      Zero Imputation             Undersampling   
 134      Zero Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Undersampling...  
 1    Gradient Boosting Machines (GBM)_Undersampling...  
 2    Gradient Boosting Machines (GBM)_Undersampling...  
 3    Gradient Boosting Machines (GBM)_Undersampling...  
 4    Gradient Boosting Machines (GBM)_Undersampling...  
 5    Gradient Boosting Machines (GBM)_Undersampling...  
 6    Gradient Boosting Machines (GBM)_Undersampling...  
 7    Gradient Boosting Machines (GBM)_Undersampling...  
 8    Gradient Boosting Machines (GBM)_Undersampling...  
 9    Gradient Boosting Machines (GBM)_Undersampling...  
 10   Gradient Boosting Machines (GBM)_Undersampling...  
 11   Gradient Boosting Machines (GBM)_Undersampling...  
 12   Gradient Boosting Machines (GBM)_Undersampling...  
 13   Gradient Boosting Machines (GBM)_Undersampling...  
 14   Gradient Boosting Machines (GBM)_Undersampling...  
 15   Gradient Boosting Machines (GBM)_Undersampling...  
 16   Gradient Boosting Machines (GBM)_Undersampling...  
 17   Gradient Boosting Machines (GBM)_Undersampling...  
 18   Gradient Boosting Machines (GBM)_Undersampling...  
 19   Gradient Boosting Machines (GBM)_Undersampling...  
 20   Gradient Boosting Machines (GBM)_Undersampling...  
 21   Gradient Boosting Machines (GBM)_Undersampling...  
 22   Gradient Boosting Machines (GBM)_Undersampling...  
 23   Gradient Boosting Machines (GBM)_Undersampling...  
 24   Gradient Boosting Machines (GBM)_Undersampling...  
 25   Gradient Boosting Machines (GBM)_Undersampling...  
 26   Gradient Boosting Machines (GBM)_Undersampling...  
 27   Gradient Boosting Machines (GBM)_Undersampling...  
 28   Gradient Boosting Machines (GBM)_Undersampling...  
 29   Gradient Boosting Machines (GBM)_Undersampling...  
 30   Gradient Boosting Machines (GBM)_Undersampling...  
 31   Gradient Boosting Machines (GBM)_Undersampling...  
 32   Gradient Boosting Machines (GBM)_Undersampling...  
 33   Gradient Boosting Machines (GBM)_Undersampling...  
 34   Gradient Boosting Machines (GBM)_Undersampling...  
 35   Gradient Boosting Machines (GBM)_Undersampling...  
 36   Gradient Boosting Machines (GBM)_Undersampling...  
 37   Gradient Boosting Machines (GBM)_Undersampling...  
 38   Gradient Boosting Machines (GBM)_Undersampling...  
 39   Gradient Boosting Machines (GBM)_Undersampling...  
 40   Gradient Boosting Machines (GBM)_Undersampling...  
 41   Gradient Boosting Machines (GBM)_Undersampling...  
 42   Gradient Boosting Machines (GBM)_Undersampling...  
 43   Gradient Boosting Machines (GBM)_Undersampling...  
 44   Gradient Boosting Machines (GBM)_Undersampling...  
 45   Gradient Boosting Machines (GBM)_Undersampling...  
 46   Gradient Boosting Machines (GBM)_Undersampling...  
 47   Gradient Boosting Machines (GBM)_Undersampling...  
 48   Gradient Boosting Machines (GBM)_Undersampling...  
 49   Gradient Boosting Machines (GBM)_Undersampling...  
 50   Gradient Boosting Machines (GBM)_Undersampling...  
 51   Gradient Boosting Machines (GBM)_Undersampling...  
 52   Gradient Boosting Machines (GBM)_Undersampling...  
 53   Gradient Boosting Machines (GBM)_Undersampling...  
 54   Gradient Boosting Machines (GBM)_Undersampling...  
 55   Gradient Boosting Machines (GBM)_Undersampling...  
 56   Gradient Boosting Machines (GBM)_Undersampling...  
 57   Gradient Boosting Machines (GBM)_Undersampling...  
 58   Gradient Boosting Machines (GBM)_Undersampling...  
 59   Gradient Boosting Machines (GBM)_Undersampling...  
 60   Gradient Boosting Machines (GBM)_Undersampling...  
 61   Gradient Boosting Machines (GBM)_Undersampling...  
 62   Gradient Boosting Machines (GBM)_Undersampling...  
 63   Gradient Boosting Machines (GBM)_Undersampling...  
 64   Gradient Boosting Machines (GBM)_Undersampling...  
 65   Gradient Boosting Machines (GBM)_Undersampling...  
 66   Gradient Boosting Machines (GBM)_Undersampling...  
 67   Gradient Boosting Machines (GBM)_Undersampling...  
 68   Gradient Boosting Machines (GBM)_Undersampling...  
 69   Gradient Boosting Machines (GBM)_Undersampling...  
 70   Gradient Boosting Machines (GBM)_Undersampling...  
 71   Gradient Boosting Machines (GBM)_Undersampling...  
 72   Gradient Boosting Machines (GBM)_Undersampling...  
 73   Gradient Boosting Machines (GBM)_Undersampling...  
 74   Gradient Boosting Machines (GBM)_Undersampling...  
 75   Gradient Boosting Machines (GBM)_Undersampling...  
 76   Gradient Boosting Machines (GBM)_Undersampling...  
 77   Gradient Boosting Machines (GBM)_Undersampling...  
 78   Gradient Boosting Machines (GBM)_Undersampling...  
 79   Gradient Boosting Machines (GBM)_Undersampling...  
 80   Gradient Boosting Machines (GBM)_Undersampling...  
 81   Gradient Boosting Machines (GBM)_Undersampling...  
 82   Gradient Boosting Machines (GBM)_Undersampling...  
 83   Gradient Boosting Machines (GBM)_Undersampling...  
 84   Gradient Boosting Machines (GBM)_Undersampling...  
 85   Gradient Boosting Machines (GBM)_Undersampling...  
 86   Gradient Boosting Machines (GBM)_Undersampling...  
 87   Gradient Boosting Machines (GBM)_Undersampling...  
 88   Gradient Boosting Machines (GBM)_Undersampling...  
 89   Gradient Boosting Machines (GBM)_Undersampling...  
 90   Gradient Boosting Machines (GBM)_Undersampling...  
 91   Gradient Boosting Machines (GBM)_Undersampling...  
 92   Gradient Boosting Machines (GBM)_Undersampling...  
 93   Gradient Boosting Machines (GBM)_Undersampling...  
 94   Gradient Boosting Machines (GBM)_Undersampling...  
 95   Gradient Boosting Machines (GBM)_Undersampling...  
 96   Gradient Boosting Machines (GBM)_Undersampling...  
 97   Gradient Boosting Machines (GBM)_Undersampling...  
 98   Gradient Boosting Machines (GBM)_Undersampling...  
 99   Gradient Boosting Machines (GBM)_Undersampling...  
 100  Gradient Boosting Machines (GBM)_Undersampling...  
 101  Gradient Boosting Machines (GBM)_Undersampling...  
 102  Gradient Boosting Machines (GBM)_Undersampling...  
 103  Gradient Boosting Machines (GBM)_Undersampling...  
 104  Gradient Boosting Machines (GBM)_Undersampling...  
 105  Gradient Boosting Machines (GBM)_Undersampling...  
 106  Gradient Boosting Machines (GBM)_Undersampling...  
 107  Gradient Boosting Machines (GBM)_Undersampling...  
 108  Gradient Boosting Machines (GBM)_Undersampling...  
 109  Gradient Boosting Machines (GBM)_Undersampling...  
 110  Gradient Boosting Machines (GBM)_Undersampling...  
 111  Gradient Boosting Machines (GBM)_Undersampling...  
 112  Gradient Boosting Machines (GBM)_Undersampling...  
 113  Gradient Boosting Machines (GBM)_Undersampling...  
 114  Gradient Boosting Machines (GBM)_Undersampling...  
 115  Gradient Boosting Machines (GBM)_Undersampling...  
 116  Gradient Boosting Machines (GBM)_Undersampling...  
 117  Gradient Boosting Machines (GBM)_Undersampling...  
 118  Gradient Boosting Machines (GBM)_Undersampling...  
 119  Gradient Boosting Machines (GBM)_Undersampling...  
 120  Gradient Boosting Machines (GBM)_Undersampling...  
 121  Gradient Boosting Machines (GBM)_Undersampling...  
 122  Gradient Boosting Machines (GBM)_Undersampling...  
 123  Gradient Boosting Machines (GBM)_Undersampling...  
 124  Gradient Boosting Machines (GBM)_Undersampling...  
 125  Gradient Boosting Machines (GBM)_Undersampling...  
 126  Gradient Boosting Machines (GBM)_Undersampling...  
 127  Gradient Boosting Machines (GBM)_Undersampling...  
 128  Gradient Boosting Machines (GBM)_Undersampling...  
 129  Gradient Boosting Machines (GBM)_Undersampling...  
 130  Gradient Boosting Machines (GBM)_Undersampling...  
 131  Gradient Boosting Machines (GBM)_Undersampling...  
 132  Gradient Boosting Machines (GBM)_Undersampling...  
 133  Gradient Boosting Machines (GBM)_Undersampling...  
 134  Gradient Boosting Machines (GBM)_Undersampling...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.621543             NaN   
 1    Gradient Boosting Machines (GBM)  0.103175             NaN   
 2    Gradient Boosting Machines (GBM)  0.658228             NaN   
 3    Gradient Boosting Machines (GBM)  0.178388             NaN   
 4    Gradient Boosting Machines (GBM)  0.691887             NaN   
 5    Gradient Boosting Machines (GBM)  0.294703             NaN   
 6    Gradient Boosting Machines (GBM)  0.383773             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.622860             NaN   
 28   Gradient Boosting Machines (GBM)  0.075067             NaN   
 29   Gradient Boosting Machines (GBM)  0.682927             NaN   
 30   Gradient Boosting Machines (GBM)  0.135266             NaN   
 31   Gradient Boosting Machines (GBM)  0.698037             NaN   
 32   Gradient Boosting Machines (GBM)  0.342400             NaN   
 33   Gradient Boosting Machines (GBM)  0.396075             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.612852             NaN   
 55   Gradient Boosting Machines (GBM)  0.079344             NaN   
 56   Gradient Boosting Machines (GBM)  0.647059             NaN   
 57   Gradient Boosting Machines (GBM)  0.141355             NaN   
 58   Gradient Boosting Machines (GBM)  0.677596             NaN   
 59   Gradient Boosting Machines (GBM)  0.271317             NaN   
 60   Gradient Boosting Machines (GBM)  0.355193             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.614331             NaN   
 82   Gradient Boosting Machines (GBM)  0.092545             NaN   
 83   Gradient Boosting Machines (GBM)  0.734694             NaN   
 84   Gradient Boosting Machines (GBM)  0.164384             NaN   
 85   Gradient Boosting Machines (GBM)  0.705125             NaN   
 86   Gradient Boosting Machines (GBM)  0.349314             NaN   
 87   Gradient Boosting Machines (GBM)  0.410249             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.627766             NaN   
 109  Gradient Boosting Machines (GBM)  0.100734             NaN   
 110  Gradient Boosting Machines (GBM)  0.699074             NaN   
 111  Gradient Boosting Machines (GBM)  0.176093             NaN   
 112  Gradient Boosting Machines (GBM)  0.711324             NaN   
 113  Gradient Boosting Machines (GBM)  0.326407             NaN   
 114  Gradient Boosting Machines (GBM)  0.422649             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0        Mean Imputation             Undersampling   
 1        Mean Imputation             Undersampling   
 2        Mean Imputation             Undersampling   
 3        Mean Imputation             Undersampling   
 4        Mean Imputation             Undersampling   
 5        Mean Imputation             Undersampling   
 6        Mean Imputation             Undersampling   
 7        Mean Imputation             Undersampling   
 8        Mean Imputation             Undersampling   
 9        Mean Imputation             Undersampling   
 10       Mean Imputation             Undersampling   
 11       Mean Imputation             Undersampling   
 12       Mean Imputation             Undersampling   
 13       Mean Imputation             Undersampling   
 14       Mean Imputation             Undersampling   
 15       Mean Imputation             Undersampling   
 16       Mean Imputation             Undersampling   
 17       Mean Imputation             Undersampling   
 18       Mean Imputation             Undersampling   
 19       Mean Imputation             Undersampling   
 20       Mean Imputation             Undersampling   
 21       Mean Imputation             Undersampling   
 22       Mean Imputation             Undersampling   
 23       Mean Imputation             Undersampling   
 24       Mean Imputation             Undersampling   
 25       Mean Imputation             Undersampling   
 26       Mean Imputation             Undersampling   
 27       Mean Imputation             Undersampling   
 28       Mean Imputation             Undersampling   
 29       Mean Imputation             Undersampling   
 30       Mean Imputation             Undersampling   
 31       Mean Imputation             Undersampling   
 32       Mean Imputation             Undersampling   
 33       Mean Imputation             Undersampling   
 34       Mean Imputation             Undersampling   
 35       Mean Imputation             Undersampling   
 36       Mean Imputation             Undersampling   
 37       Mean Imputation             Undersampling   
 38       Mean Imputation             Undersampling   
 39       Mean Imputation             Undersampling   
 40       Mean Imputation             Undersampling   
 41       Mean Imputation             Undersampling   
 42       Mean Imputation             Undersampling   
 43       Mean Imputation             Undersampling   
 44       Mean Imputation             Undersampling   
 45       Mean Imputation             Undersampling   
 46       Mean Imputation             Undersampling   
 47       Mean Imputation             Undersampling   
 48       Mean Imputation             Undersampling   
 49       Mean Imputation             Undersampling   
 50       Mean Imputation             Undersampling   
 51       Mean Imputation             Undersampling   
 52       Mean Imputation             Undersampling   
 53       Mean Imputation             Undersampling   
 54       Mean Imputation             Undersampling   
 55       Mean Imputation             Undersampling   
 56       Mean Imputation             Undersampling   
 57       Mean Imputation             Undersampling   
 58       Mean Imputation             Undersampling   
 59       Mean Imputation             Undersampling   
 60       Mean Imputation             Undersampling   
 61       Mean Imputation             Undersampling   
 62       Mean Imputation             Undersampling   
 63       Mean Imputation             Undersampling   
 64       Mean Imputation             Undersampling   
 65       Mean Imputation             Undersampling   
 66       Mean Imputation             Undersampling   
 67       Mean Imputation             Undersampling   
 68       Mean Imputation             Undersampling   
 69       Mean Imputation             Undersampling   
 70       Mean Imputation             Undersampling   
 71       Mean Imputation             Undersampling   
 72       Mean Imputation             Undersampling   
 73       Mean Imputation             Undersampling   
 74       Mean Imputation             Undersampling   
 75       Mean Imputation             Undersampling   
 76       Mean Imputation             Undersampling   
 77       Mean Imputation             Undersampling   
 78       Mean Imputation             Undersampling   
 79       Mean Imputation             Undersampling   
 80       Mean Imputation             Undersampling   
 81       Mean Imputation             Undersampling   
 82       Mean Imputation             Undersampling   
 83       Mean Imputation             Undersampling   
 84       Mean Imputation             Undersampling   
 85       Mean Imputation             Undersampling   
 86       Mean Imputation             Undersampling   
 87       Mean Imputation             Undersampling   
 88       Mean Imputation             Undersampling   
 89       Mean Imputation             Undersampling   
 90       Mean Imputation             Undersampling   
 91       Mean Imputation             Undersampling   
 92       Mean Imputation             Undersampling   
 93       Mean Imputation             Undersampling   
 94       Mean Imputation             Undersampling   
 95       Mean Imputation             Undersampling   
 96       Mean Imputation             Undersampling   
 97       Mean Imputation             Undersampling   
 98       Mean Imputation             Undersampling   
 99       Mean Imputation             Undersampling   
 100      Mean Imputation             Undersampling   
 101      Mean Imputation             Undersampling   
 102      Mean Imputation             Undersampling   
 103      Mean Imputation             Undersampling   
 104      Mean Imputation             Undersampling   
 105      Mean Imputation             Undersampling   
 106      Mean Imputation             Undersampling   
 107      Mean Imputation             Undersampling   
 108      Mean Imputation             Undersampling   
 109      Mean Imputation             Undersampling   
 110      Mean Imputation             Undersampling   
 111      Mean Imputation             Undersampling   
 112      Mean Imputation             Undersampling   
 113      Mean Imputation             Undersampling   
 114      Mean Imputation             Undersampling   
 115      Mean Imputation             Undersampling   
 116      Mean Imputation             Undersampling   
 117      Mean Imputation             Undersampling   
 118      Mean Imputation             Undersampling   
 119      Mean Imputation             Undersampling   
 120      Mean Imputation             Undersampling   
 121      Mean Imputation             Undersampling   
 122      Mean Imputation             Undersampling   
 123      Mean Imputation             Undersampling   
 124      Mean Imputation             Undersampling   
 125      Mean Imputation             Undersampling   
 126      Mean Imputation             Undersampling   
 127      Mean Imputation             Undersampling   
 128      Mean Imputation             Undersampling   
 129      Mean Imputation             Undersampling   
 130      Mean Imputation             Undersampling   
 131      Mean Imputation             Undersampling   
 132      Mean Imputation             Undersampling   
 133      Mean Imputation             Undersampling   
 134      Mean Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Undersampling...  
 1    Gradient Boosting Machines (GBM)_Undersampling...  
 2    Gradient Boosting Machines (GBM)_Undersampling...  
 3    Gradient Boosting Machines (GBM)_Undersampling...  
 4    Gradient Boosting Machines (GBM)_Undersampling...  
 5    Gradient Boosting Machines (GBM)_Undersampling...  
 6    Gradient Boosting Machines (GBM)_Undersampling...  
 7    Gradient Boosting Machines (GBM)_Undersampling...  
 8    Gradient Boosting Machines (GBM)_Undersampling...  
 9    Gradient Boosting Machines (GBM)_Undersampling...  
 10   Gradient Boosting Machines (GBM)_Undersampling...  
 11   Gradient Boosting Machines (GBM)_Undersampling...  
 12   Gradient Boosting Machines (GBM)_Undersampling...  
 13   Gradient Boosting Machines (GBM)_Undersampling...  
 14   Gradient Boosting Machines (GBM)_Undersampling...  
 15   Gradient Boosting Machines (GBM)_Undersampling...  
 16   Gradient Boosting Machines (GBM)_Undersampling...  
 17   Gradient Boosting Machines (GBM)_Undersampling...  
 18   Gradient Boosting Machines (GBM)_Undersampling...  
 19   Gradient Boosting Machines (GBM)_Undersampling...  
 20   Gradient Boosting Machines (GBM)_Undersampling...  
 21   Gradient Boosting Machines (GBM)_Undersampling...  
 22   Gradient Boosting Machines (GBM)_Undersampling...  
 23   Gradient Boosting Machines (GBM)_Undersampling...  
 24   Gradient Boosting Machines (GBM)_Undersampling...  
 25   Gradient Boosting Machines (GBM)_Undersampling...  
 26   Gradient Boosting Machines (GBM)_Undersampling...  
 27   Gradient Boosting Machines (GBM)_Undersampling...  
 28   Gradient Boosting Machines (GBM)_Undersampling...  
 29   Gradient Boosting Machines (GBM)_Undersampling...  
 30   Gradient Boosting Machines (GBM)_Undersampling...  
 31   Gradient Boosting Machines (GBM)_Undersampling...  
 32   Gradient Boosting Machines (GBM)_Undersampling...  
 33   Gradient Boosting Machines (GBM)_Undersampling...  
 34   Gradient Boosting Machines (GBM)_Undersampling...  
 35   Gradient Boosting Machines (GBM)_Undersampling...  
 36   Gradient Boosting Machines (GBM)_Undersampling...  
 37   Gradient Boosting Machines (GBM)_Undersampling...  
 38   Gradient Boosting Machines (GBM)_Undersampling...  
 39   Gradient Boosting Machines (GBM)_Undersampling...  
 40   Gradient Boosting Machines (GBM)_Undersampling...  
 41   Gradient Boosting Machines (GBM)_Undersampling...  
 42   Gradient Boosting Machines (GBM)_Undersampling...  
 43   Gradient Boosting Machines (GBM)_Undersampling...  
 44   Gradient Boosting Machines (GBM)_Undersampling...  
 45   Gradient Boosting Machines (GBM)_Undersampling...  
 46   Gradient Boosting Machines (GBM)_Undersampling...  
 47   Gradient Boosting Machines (GBM)_Undersampling...  
 48   Gradient Boosting Machines (GBM)_Undersampling...  
 49   Gradient Boosting Machines (GBM)_Undersampling...  
 50   Gradient Boosting Machines (GBM)_Undersampling...  
 51   Gradient Boosting Machines (GBM)_Undersampling...  
 52   Gradient Boosting Machines (GBM)_Undersampling...  
 53   Gradient Boosting Machines (GBM)_Undersampling...  
 54   Gradient Boosting Machines (GBM)_Undersampling...  
 55   Gradient Boosting Machines (GBM)_Undersampling...  
 56   Gradient Boosting Machines (GBM)_Undersampling...  
 57   Gradient Boosting Machines (GBM)_Undersampling...  
 58   Gradient Boosting Machines (GBM)_Undersampling...  
 59   Gradient Boosting Machines (GBM)_Undersampling...  
 60   Gradient Boosting Machines (GBM)_Undersampling...  
 61   Gradient Boosting Machines (GBM)_Undersampling...  
 62   Gradient Boosting Machines (GBM)_Undersampling...  
 63   Gradient Boosting Machines (GBM)_Undersampling...  
 64   Gradient Boosting Machines (GBM)_Undersampling...  
 65   Gradient Boosting Machines (GBM)_Undersampling...  
 66   Gradient Boosting Machines (GBM)_Undersampling...  
 67   Gradient Boosting Machines (GBM)_Undersampling...  
 68   Gradient Boosting Machines (GBM)_Undersampling...  
 69   Gradient Boosting Machines (GBM)_Undersampling...  
 70   Gradient Boosting Machines (GBM)_Undersampling...  
 71   Gradient Boosting Machines (GBM)_Undersampling...  
 72   Gradient Boosting Machines (GBM)_Undersampling...  
 73   Gradient Boosting Machines (GBM)_Undersampling...  
 74   Gradient Boosting Machines (GBM)_Undersampling...  
 75   Gradient Boosting Machines (GBM)_Undersampling...  
 76   Gradient Boosting Machines (GBM)_Undersampling...  
 77   Gradient Boosting Machines (GBM)_Undersampling...  
 78   Gradient Boosting Machines (GBM)_Undersampling...  
 79   Gradient Boosting Machines (GBM)_Undersampling...  
 80   Gradient Boosting Machines (GBM)_Undersampling...  
 81   Gradient Boosting Machines (GBM)_Undersampling...  
 82   Gradient Boosting Machines (GBM)_Undersampling...  
 83   Gradient Boosting Machines (GBM)_Undersampling...  
 84   Gradient Boosting Machines (GBM)_Undersampling...  
 85   Gradient Boosting Machines (GBM)_Undersampling...  
 86   Gradient Boosting Machines (GBM)_Undersampling...  
 87   Gradient Boosting Machines (GBM)_Undersampling...  
 88   Gradient Boosting Machines (GBM)_Undersampling...  
 89   Gradient Boosting Machines (GBM)_Undersampling...  
 90   Gradient Boosting Machines (GBM)_Undersampling...  
 91   Gradient Boosting Machines (GBM)_Undersampling...  
 92   Gradient Boosting Machines (GBM)_Undersampling...  
 93   Gradient Boosting Machines (GBM)_Undersampling...  
 94   Gradient Boosting Machines (GBM)_Undersampling...  
 95   Gradient Boosting Machines (GBM)_Undersampling...  
 96   Gradient Boosting Machines (GBM)_Undersampling...  
 97   Gradient Boosting Machines (GBM)_Undersampling...  
 98   Gradient Boosting Machines (GBM)_Undersampling...  
 99   Gradient Boosting Machines (GBM)_Undersampling...  
 100  Gradient Boosting Machines (GBM)_Undersampling...  
 101  Gradient Boosting Machines (GBM)_Undersampling...  
 102  Gradient Boosting Machines (GBM)_Undersampling...  
 103  Gradient Boosting Machines (GBM)_Undersampling...  
 104  Gradient Boosting Machines (GBM)_Undersampling...  
 105  Gradient Boosting Machines (GBM)_Undersampling...  
 106  Gradient Boosting Machines (GBM)_Undersampling...  
 107  Gradient Boosting Machines (GBM)_Undersampling...  
 108  Gradient Boosting Machines (GBM)_Undersampling...  
 109  Gradient Boosting Machines (GBM)_Undersampling...  
 110  Gradient Boosting Machines (GBM)_Undersampling...  
 111  Gradient Boosting Machines (GBM)_Undersampling...  
 112  Gradient Boosting Machines (GBM)_Undersampling...  
 113  Gradient Boosting Machines (GBM)_Undersampling...  
 114  Gradient Boosting Machines (GBM)_Undersampling...  
 115  Gradient Boosting Machines (GBM)_Undersampling...  
 116  Gradient Boosting Machines (GBM)_Undersampling...  
 117  Gradient Boosting Machines (GBM)_Undersampling...  
 118  Gradient Boosting Machines (GBM)_Undersampling...  
 119  Gradient Boosting Machines (GBM)_Undersampling...  
 120  Gradient Boosting Machines (GBM)_Undersampling...  
 121  Gradient Boosting Machines (GBM)_Undersampling...  
 122  Gradient Boosting Machines (GBM)_Undersampling...  
 123  Gradient Boosting Machines (GBM)_Undersampling...  
 124  Gradient Boosting Machines (GBM)_Undersampling...  
 125  Gradient Boosting Machines (GBM)_Undersampling...  
 126  Gradient Boosting Machines (GBM)_Undersampling...  
 127  Gradient Boosting Machines (GBM)_Undersampling...  
 128  Gradient Boosting Machines (GBM)_Undersampling...  
 129  Gradient Boosting Machines (GBM)_Undersampling...  
 130  Gradient Boosting Machines (GBM)_Undersampling...  
 131  Gradient Boosting Machines (GBM)_Undersampling...  
 132  Gradient Boosting Machines (GBM)_Undersampling...  
 133  Gradient Boosting Machines (GBM)_Undersampling...  
 134  Gradient Boosting Machines (GBM)_Undersampling...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.629181             NaN   
 1    Gradient Boosting Machines (GBM)  0.104124             NaN   
 2    Gradient Boosting Machines (GBM)  0.649789             NaN   
 3    Gradient Boosting Machines (GBM)  0.179487             NaN   
 4    Gradient Boosting Machines (GBM)  0.691467             NaN   
 5    Gradient Boosting Machines (GBM)  0.292169             NaN   
 6    Gradient Boosting Machines (GBM)  0.382934             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.597050             NaN   
 28   Gradient Boosting Machines (GBM)  0.071518             NaN   
 29   Gradient Boosting Machines (GBM)  0.695122             NaN   
 30   Gradient Boosting Machines (GBM)  0.129693             NaN   
 31   Gradient Boosting Machines (GBM)  0.695282             NaN   
 32   Gradient Boosting Machines (GBM)  0.314067             NaN   
 33   Gradient Boosting Machines (GBM)  0.390564             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.584145             NaN   
 55   Gradient Boosting Machines (GBM)  0.080723             NaN   
 56   Gradient Boosting Machines (GBM)  0.716578             NaN   
 57   Gradient Boosting Machines (GBM)  0.145100             NaN   
 58   Gradient Boosting Machines (GBM)  0.694298             NaN   
 59   Gradient Boosting Machines (GBM)  0.306750             NaN   
 60   Gradient Boosting Machines (GBM)  0.388597             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.613277             NaN   
 82   Gradient Boosting Machines (GBM)  0.087549             NaN   
 83   Gradient Boosting Machines (GBM)  0.688776             NaN   
 84   Gradient Boosting Machines (GBM)  0.155351             NaN   
 85   Gradient Boosting Machines (GBM)  0.703506             NaN   
 86   Gradient Boosting Machines (GBM)  0.329615             NaN   
 87   Gradient Boosting Machines (GBM)  0.407011             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.639621             NaN   
 109  Gradient Boosting Machines (GBM)  0.101108             NaN   
 110  Gradient Boosting Machines (GBM)  0.675926             NaN   
 111  Gradient Boosting Machines (GBM)  0.175904             NaN   
 112  Gradient Boosting Machines (GBM)  0.711200             NaN   
 113  Gradient Boosting Machines (GBM)  0.328171             NaN   
 114  Gradient Boosting Machines (GBM)  0.422399             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique Imbalance Class Technique  \
 0      Median Imputation             Undersampling   
 1      Median Imputation             Undersampling   
 2      Median Imputation             Undersampling   
 3      Median Imputation             Undersampling   
 4      Median Imputation             Undersampling   
 5      Median Imputation             Undersampling   
 6      Median Imputation             Undersampling   
 7      Median Imputation             Undersampling   
 8      Median Imputation             Undersampling   
 9      Median Imputation             Undersampling   
 10     Median Imputation             Undersampling   
 11     Median Imputation             Undersampling   
 12     Median Imputation             Undersampling   
 13     Median Imputation             Undersampling   
 14     Median Imputation             Undersampling   
 15     Median Imputation             Undersampling   
 16     Median Imputation             Undersampling   
 17     Median Imputation             Undersampling   
 18     Median Imputation             Undersampling   
 19     Median Imputation             Undersampling   
 20     Median Imputation             Undersampling   
 21     Median Imputation             Undersampling   
 22     Median Imputation             Undersampling   
 23     Median Imputation             Undersampling   
 24     Median Imputation             Undersampling   
 25     Median Imputation             Undersampling   
 26     Median Imputation             Undersampling   
 27     Median Imputation             Undersampling   
 28     Median Imputation             Undersampling   
 29     Median Imputation             Undersampling   
 30     Median Imputation             Undersampling   
 31     Median Imputation             Undersampling   
 32     Median Imputation             Undersampling   
 33     Median Imputation             Undersampling   
 34     Median Imputation             Undersampling   
 35     Median Imputation             Undersampling   
 36     Median Imputation             Undersampling   
 37     Median Imputation             Undersampling   
 38     Median Imputation             Undersampling   
 39     Median Imputation             Undersampling   
 40     Median Imputation             Undersampling   
 41     Median Imputation             Undersampling   
 42     Median Imputation             Undersampling   
 43     Median Imputation             Undersampling   
 44     Median Imputation             Undersampling   
 45     Median Imputation             Undersampling   
 46     Median Imputation             Undersampling   
 47     Median Imputation             Undersampling   
 48     Median Imputation             Undersampling   
 49     Median Imputation             Undersampling   
 50     Median Imputation             Undersampling   
 51     Median Imputation             Undersampling   
 52     Median Imputation             Undersampling   
 53     Median Imputation             Undersampling   
 54     Median Imputation             Undersampling   
 55     Median Imputation             Undersampling   
 56     Median Imputation             Undersampling   
 57     Median Imputation             Undersampling   
 58     Median Imputation             Undersampling   
 59     Median Imputation             Undersampling   
 60     Median Imputation             Undersampling   
 61     Median Imputation             Undersampling   
 62     Median Imputation             Undersampling   
 63     Median Imputation             Undersampling   
 64     Median Imputation             Undersampling   
 65     Median Imputation             Undersampling   
 66     Median Imputation             Undersampling   
 67     Median Imputation             Undersampling   
 68     Median Imputation             Undersampling   
 69     Median Imputation             Undersampling   
 70     Median Imputation             Undersampling   
 71     Median Imputation             Undersampling   
 72     Median Imputation             Undersampling   
 73     Median Imputation             Undersampling   
 74     Median Imputation             Undersampling   
 75     Median Imputation             Undersampling   
 76     Median Imputation             Undersampling   
 77     Median Imputation             Undersampling   
 78     Median Imputation             Undersampling   
 79     Median Imputation             Undersampling   
 80     Median Imputation             Undersampling   
 81     Median Imputation             Undersampling   
 82     Median Imputation             Undersampling   
 83     Median Imputation             Undersampling   
 84     Median Imputation             Undersampling   
 85     Median Imputation             Undersampling   
 86     Median Imputation             Undersampling   
 87     Median Imputation             Undersampling   
 88     Median Imputation             Undersampling   
 89     Median Imputation             Undersampling   
 90     Median Imputation             Undersampling   
 91     Median Imputation             Undersampling   
 92     Median Imputation             Undersampling   
 93     Median Imputation             Undersampling   
 94     Median Imputation             Undersampling   
 95     Median Imputation             Undersampling   
 96     Median Imputation             Undersampling   
 97     Median Imputation             Undersampling   
 98     Median Imputation             Undersampling   
 99     Median Imputation             Undersampling   
 100    Median Imputation             Undersampling   
 101    Median Imputation             Undersampling   
 102    Median Imputation             Undersampling   
 103    Median Imputation             Undersampling   
 104    Median Imputation             Undersampling   
 105    Median Imputation             Undersampling   
 106    Median Imputation             Undersampling   
 107    Median Imputation             Undersampling   
 108    Median Imputation             Undersampling   
 109    Median Imputation             Undersampling   
 110    Median Imputation             Undersampling   
 111    Median Imputation             Undersampling   
 112    Median Imputation             Undersampling   
 113    Median Imputation             Undersampling   
 114    Median Imputation             Undersampling   
 115    Median Imputation             Undersampling   
 116    Median Imputation             Undersampling   
 117    Median Imputation             Undersampling   
 118    Median Imputation             Undersampling   
 119    Median Imputation             Undersampling   
 120    Median Imputation             Undersampling   
 121    Median Imputation             Undersampling   
 122    Median Imputation             Undersampling   
 123    Median Imputation             Undersampling   
 124    Median Imputation             Undersampling   
 125    Median Imputation             Undersampling   
 126    Median Imputation             Undersampling   
 127    Median Imputation             Undersampling   
 128    Median Imputation             Undersampling   
 129    Median Imputation             Undersampling   
 130    Median Imputation             Undersampling   
 131    Median Imputation             Undersampling   
 132    Median Imputation             Undersampling   
 133    Median Imputation             Undersampling   
 134    Median Imputation             Undersampling   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Undersampling...  
 1    Gradient Boosting Machines (GBM)_Undersampling...  
 2    Gradient Boosting Machines (GBM)_Undersampling...  
 3    Gradient Boosting Machines (GBM)_Undersampling...  
 4    Gradient Boosting Machines (GBM)_Undersampling...  
 5    Gradient Boosting Machines (GBM)_Undersampling...  
 6    Gradient Boosting Machines (GBM)_Undersampling...  
 7    Gradient Boosting Machines (GBM)_Undersampling...  
 8    Gradient Boosting Machines (GBM)_Undersampling...  
 9    Gradient Boosting Machines (GBM)_Undersampling...  
 10   Gradient Boosting Machines (GBM)_Undersampling...  
 11   Gradient Boosting Machines (GBM)_Undersampling...  
 12   Gradient Boosting Machines (GBM)_Undersampling...  
 13   Gradient Boosting Machines (GBM)_Undersampling...  
 14   Gradient Boosting Machines (GBM)_Undersampling...  
 15   Gradient Boosting Machines (GBM)_Undersampling...  
 16   Gradient Boosting Machines (GBM)_Undersampling...  
 17   Gradient Boosting Machines (GBM)_Undersampling...  
 18   Gradient Boosting Machines (GBM)_Undersampling...  
 19   Gradient Boosting Machines (GBM)_Undersampling...  
 20   Gradient Boosting Machines (GBM)_Undersampling...  
 21   Gradient Boosting Machines (GBM)_Undersampling...  
 22   Gradient Boosting Machines (GBM)_Undersampling...  
 23   Gradient Boosting Machines (GBM)_Undersampling...  
 24   Gradient Boosting Machines (GBM)_Undersampling...  
 25   Gradient Boosting Machines (GBM)_Undersampling...  
 26   Gradient Boosting Machines (GBM)_Undersampling...  
 27   Gradient Boosting Machines (GBM)_Undersampling...  
 28   Gradient Boosting Machines (GBM)_Undersampling...  
 29   Gradient Boosting Machines (GBM)_Undersampling...  
 30   Gradient Boosting Machines (GBM)_Undersampling...  
 31   Gradient Boosting Machines (GBM)_Undersampling...  
 32   Gradient Boosting Machines (GBM)_Undersampling...  
 33   Gradient Boosting Machines (GBM)_Undersampling...  
 34   Gradient Boosting Machines (GBM)_Undersampling...  
 35   Gradient Boosting Machines (GBM)_Undersampling...  
 36   Gradient Boosting Machines (GBM)_Undersampling...  
 37   Gradient Boosting Machines (GBM)_Undersampling...  
 38   Gradient Boosting Machines (GBM)_Undersampling...  
 39   Gradient Boosting Machines (GBM)_Undersampling...  
 40   Gradient Boosting Machines (GBM)_Undersampling...  
 41   Gradient Boosting Machines (GBM)_Undersampling...  
 42   Gradient Boosting Machines (GBM)_Undersampling...  
 43   Gradient Boosting Machines (GBM)_Undersampling...  
 44   Gradient Boosting Machines (GBM)_Undersampling...  
 45   Gradient Boosting Machines (GBM)_Undersampling...  
 46   Gradient Boosting Machines (GBM)_Undersampling...  
 47   Gradient Boosting Machines (GBM)_Undersampling...  
 48   Gradient Boosting Machines (GBM)_Undersampling...  
 49   Gradient Boosting Machines (GBM)_Undersampling...  
 50   Gradient Boosting Machines (GBM)_Undersampling...  
 51   Gradient Boosting Machines (GBM)_Undersampling...  
 52   Gradient Boosting Machines (GBM)_Undersampling...  
 53   Gradient Boosting Machines (GBM)_Undersampling...  
 54   Gradient Boosting Machines (GBM)_Undersampling...  
 55   Gradient Boosting Machines (GBM)_Undersampling...  
 56   Gradient Boosting Machines (GBM)_Undersampling...  
 57   Gradient Boosting Machines (GBM)_Undersampling...  
 58   Gradient Boosting Machines (GBM)_Undersampling...  
 59   Gradient Boosting Machines (GBM)_Undersampling...  
 60   Gradient Boosting Machines (GBM)_Undersampling...  
 61   Gradient Boosting Machines (GBM)_Undersampling...  
 62   Gradient Boosting Machines (GBM)_Undersampling...  
 63   Gradient Boosting Machines (GBM)_Undersampling...  
 64   Gradient Boosting Machines (GBM)_Undersampling...  
 65   Gradient Boosting Machines (GBM)_Undersampling...  
 66   Gradient Boosting Machines (GBM)_Undersampling...  
 67   Gradient Boosting Machines (GBM)_Undersampling...  
 68   Gradient Boosting Machines (GBM)_Undersampling...  
 69   Gradient Boosting Machines (GBM)_Undersampling...  
 70   Gradient Boosting Machines (GBM)_Undersampling...  
 71   Gradient Boosting Machines (GBM)_Undersampling...  
 72   Gradient Boosting Machines (GBM)_Undersampling...  
 73   Gradient Boosting Machines (GBM)_Undersampling...  
 74   Gradient Boosting Machines (GBM)_Undersampling...  
 75   Gradient Boosting Machines (GBM)_Undersampling...  
 76   Gradient Boosting Machines (GBM)_Undersampling...  
 77   Gradient Boosting Machines (GBM)_Undersampling...  
 78   Gradient Boosting Machines (GBM)_Undersampling...  
 79   Gradient Boosting Machines (GBM)_Undersampling...  
 80   Gradient Boosting Machines (GBM)_Undersampling...  
 81   Gradient Boosting Machines (GBM)_Undersampling...  
 82   Gradient Boosting Machines (GBM)_Undersampling...  
 83   Gradient Boosting Machines (GBM)_Undersampling...  
 84   Gradient Boosting Machines (GBM)_Undersampling...  
 85   Gradient Boosting Machines (GBM)_Undersampling...  
 86   Gradient Boosting Machines (GBM)_Undersampling...  
 87   Gradient Boosting Machines (GBM)_Undersampling...  
 88   Gradient Boosting Machines (GBM)_Undersampling...  
 89   Gradient Boosting Machines (GBM)_Undersampling...  
 90   Gradient Boosting Machines (GBM)_Undersampling...  
 91   Gradient Boosting Machines (GBM)_Undersampling...  
 92   Gradient Boosting Machines (GBM)_Undersampling...  
 93   Gradient Boosting Machines (GBM)_Undersampling...  
 94   Gradient Boosting Machines (GBM)_Undersampling...  
 95   Gradient Boosting Machines (GBM)_Undersampling...  
 96   Gradient Boosting Machines (GBM)_Undersampling...  
 97   Gradient Boosting Machines (GBM)_Undersampling...  
 98   Gradient Boosting Machines (GBM)_Undersampling...  
 99   Gradient Boosting Machines (GBM)_Undersampling...  
 100  Gradient Boosting Machines (GBM)_Undersampling...  
 101  Gradient Boosting Machines (GBM)_Undersampling...  
 102  Gradient Boosting Machines (GBM)_Undersampling...  
 103  Gradient Boosting Machines (GBM)_Undersampling...  
 104  Gradient Boosting Machines (GBM)_Undersampling...  
 105  Gradient Boosting Machines (GBM)_Undersampling...  
 106  Gradient Boosting Machines (GBM)_Undersampling...  
 107  Gradient Boosting Machines (GBM)_Undersampling...  
 108  Gradient Boosting Machines (GBM)_Undersampling...  
 109  Gradient Boosting Machines (GBM)_Undersampling...  
 110  Gradient Boosting Machines (GBM)_Undersampling...  
 111  Gradient Boosting Machines (GBM)_Undersampling...  
 112  Gradient Boosting Machines (GBM)_Undersampling...  
 113  Gradient Boosting Machines (GBM)_Undersampling...  
 114  Gradient Boosting Machines (GBM)_Undersampling...  
 115  Gradient Boosting Machines (GBM)_Undersampling...  
 116  Gradient Boosting Machines (GBM)_Undersampling...  
 117  Gradient Boosting Machines (GBM)_Undersampling...  
 118  Gradient Boosting Machines (GBM)_Undersampling...  
 119  Gradient Boosting Machines (GBM)_Undersampling...  
 120  Gradient Boosting Machines (GBM)_Undersampling...  
 121  Gradient Boosting Machines (GBM)_Undersampling...  
 122  Gradient Boosting Machines (GBM)_Undersampling...  
 123  Gradient Boosting Machines (GBM)_Undersampling...  
 124  Gradient Boosting Machines (GBM)_Undersampling...  
 125  Gradient Boosting Machines (GBM)_Undersampling...  
 126  Gradient Boosting Machines (GBM)_Undersampling...  
 127  Gradient Boosting Machines (GBM)_Undersampling...  
 128  Gradient Boosting Machines (GBM)_Undersampling...  
 129  Gradient Boosting Machines (GBM)_Undersampling...  
 130  Gradient Boosting Machines (GBM)_Undersampling...  
 131  Gradient Boosting Machines (GBM)_Undersampling...  
 132  Gradient Boosting Machines (GBM)_Undersampling...  
 133  Gradient Boosting Machines (GBM)_Undersampling...  
 134  Gradient Boosting Machines (GBM)_Undersampling...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.811957             NaN   
 1    Gradient Boosting Machines (GBM)  0.118400             NaN   
 2    Gradient Boosting Machines (GBM)  0.312236             NaN   
 3    Gradient Boosting Machines (GBM)  0.171694             NaN   
 4    Gradient Boosting Machines (GBM)  0.668407             NaN   
 5    Gradient Boosting Machines (GBM)  0.273396             NaN   
 6    Gradient Boosting Machines (GBM)  0.336813             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.830919             NaN   
 28   Gradient Boosting Machines (GBM)  0.114516             NaN   
 29   Gradient Boosting Machines (GBM)  0.432927             NaN   
 30   Gradient Boosting Machines (GBM)  0.181122             NaN   
 31   Gradient Boosting Machines (GBM)  0.704383             NaN   
 32   Gradient Boosting Machines (GBM)  0.315976             NaN   
 33   Gradient Boosting Machines (GBM)  0.408767             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.798789             NaN   
 55   Gradient Boosting Machines (GBM)  0.095372             NaN   
 56   Gradient Boosting Machines (GBM)  0.363636             NaN   
 57   Gradient Boosting Machines (GBM)  0.151111             NaN   
 58   Gradient Boosting Machines (GBM)  0.659361             NaN   
 59   Gradient Boosting Machines (GBM)  0.267713             NaN   
 60   Gradient Boosting Machines (GBM)  0.318723             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.812698             NaN   
 82   Gradient Boosting Machines (GBM)  0.109256             NaN   
 83   Gradient Boosting Machines (GBM)  0.367347             NaN   
 84   Gradient Boosting Machines (GBM)  0.168421             NaN   
 85   Gradient Boosting Machines (GBM)  0.670480             NaN   
 86   Gradient Boosting Machines (GBM)  0.273503             NaN   
 87   Gradient Boosting Machines (GBM)  0.340961             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.814015             NaN   
 109  Gradient Boosting Machines (GBM)  0.124233             NaN   
 110  Gradient Boosting Machines (GBM)  0.375000             NaN   
 111  Gradient Boosting Machines (GBM)  0.186636             NaN   
 112  Gradient Boosting Machines (GBM)  0.688363             NaN   
 113  Gradient Boosting Machines (GBM)  0.292965             NaN   
 114  Gradient Boosting Machines (GBM)  0.376725             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 1        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 2        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 3        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 4        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 5        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 6        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 7        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 8        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 9        Zero Imputation  Hybrid Sampling (SMOTEENN)   
 10       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 11       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 12       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 13       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 14       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 15       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 16       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 17       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 18       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 19       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 20       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 21       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 22       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 23       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 24       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 25       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 26       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 27       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 28       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 29       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 30       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 31       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 32       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 33       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 34       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 35       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 36       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 37       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 38       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 39       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 40       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 41       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 42       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 43       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 44       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 45       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 46       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 47       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 48       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 49       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 50       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 51       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 52       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 53       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 54       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 55       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 56       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 57       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 58       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 59       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 60       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 61       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 62       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 63       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 64       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 65       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 66       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 67       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 68       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 69       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 70       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 71       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 72       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 73       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 74       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 75       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 76       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 77       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 78       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 79       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 80       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 81       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 82       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 83       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 84       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 85       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 86       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 87       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 88       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 89       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 90       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 91       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 92       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 93       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 94       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 95       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 96       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 97       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 98       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 99       Zero Imputation  Hybrid Sampling (SMOTEENN)   
 100      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 101      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 102      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 103      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 104      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 105      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 106      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 107      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 108      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 109      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 110      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 111      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 112      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 113      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 114      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 115      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 116      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 117      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 118      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 119      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 120      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 121      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 122      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 123      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 124      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 125      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 126      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 127      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 128      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 129      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 130      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 131      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 132      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 133      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 134      Zero Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.826969             NaN   
 1    Gradient Boosting Machines (GBM)  0.112546             NaN   
 2    Gradient Boosting Machines (GBM)  0.257384             NaN   
 3    Gradient Boosting Machines (GBM)  0.156611             NaN   
 4    Gradient Boosting Machines (GBM)  0.651866             NaN   
 5    Gradient Boosting Machines (GBM)  0.233869             NaN   
 6    Gradient Boosting Machines (GBM)  0.303732             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.828812             NaN   
 28   Gradient Boosting Machines (GBM)  0.093645             NaN   
 29   Gradient Boosting Machines (GBM)  0.341463             NaN   
 30   Gradient Boosting Machines (GBM)  0.146982             NaN   
 31   Gradient Boosting Machines (GBM)  0.678118             NaN   
 32   Gradient Boosting Machines (GBM)  0.298252             NaN   
 33   Gradient Boosting Machines (GBM)  0.356237             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.821701             NaN   
 55   Gradient Boosting Machines (GBM)  0.103560             NaN   
 56   Gradient Boosting Machines (GBM)  0.342246             NaN   
 57   Gradient Boosting Machines (GBM)  0.159006             NaN   
 58   Gradient Boosting Machines (GBM)  0.674822             NaN   
 59   Gradient Boosting Machines (GBM)  0.268507             NaN   
 60   Gradient Boosting Machines (GBM)  0.349644             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.838514             NaN   
 82   Gradient Boosting Machines (GBM)  0.113173             NaN   
 83   Gradient Boosting Machines (GBM)  0.311224             NaN   
 84   Gradient Boosting Machines (GBM)  0.165986             NaN   
 85   Gradient Boosting Machines (GBM)  0.674381             NaN   
 86   Gradient Boosting Machines (GBM)  0.276701             NaN   
 87   Gradient Boosting Machines (GBM)  0.348761             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.835090             NaN   
 109  Gradient Boosting Machines (GBM)  0.123162             NaN   
 110  Gradient Boosting Machines (GBM)  0.310185             NaN   
 111  Gradient Boosting Machines (GBM)  0.176316             NaN   
 112  Gradient Boosting Machines (GBM)  0.676027             NaN   
 113  Gradient Boosting Machines (GBM)  0.270665             NaN   
 114  Gradient Boosting Machines (GBM)  0.352054             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 1        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 2        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 3        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 4        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 5        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 6        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 7        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 8        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 9        Mean Imputation  Hybrid Sampling (SMOTEENN)   
 10       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 11       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 12       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 13       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 14       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 15       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 16       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 17       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 18       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 19       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 20       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 21       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 22       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 23       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 24       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 25       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 26       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 27       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 28       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 29       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 30       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 31       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 32       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 33       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 34       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 35       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 36       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 37       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 38       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 39       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 40       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 41       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 42       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 43       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 44       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 45       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 46       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 47       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 48       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 49       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 50       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 51       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 52       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 53       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 54       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 55       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 56       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 57       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 58       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 59       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 60       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 61       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 62       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 63       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 64       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 65       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 66       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 67       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 68       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 69       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 70       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 71       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 72       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 73       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 74       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 75       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 76       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 77       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 78       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 79       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 80       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 81       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 82       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 83       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 84       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 85       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 86       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 87       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 88       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 89       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 90       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 91       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 92       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 93       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 94       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 95       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 96       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 97       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 98       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 99       Mean Imputation  Hybrid Sampling (SMOTEENN)   
 100      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 101      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 102      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 103      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 104      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 105      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 106      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 107      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 108      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 109      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 110      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 111      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 112      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 113      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 114      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 115      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 116      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 117      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 118      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 119      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 120      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 121      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 122      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 123      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 124      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 125      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 126      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 127      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 128      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 129      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 130      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 131      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 132      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 133      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 134      Mean Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.822228             NaN   
 1    Gradient Boosting Machines (GBM)  0.118467             NaN   
 2    Gradient Boosting Machines (GBM)  0.286920             NaN   
 3    Gradient Boosting Machines (GBM)  0.167694             NaN   
 4    Gradient Boosting Machines (GBM)  0.640819             NaN   
 5    Gradient Boosting Machines (GBM)  0.218757             NaN   
 6    Gradient Boosting Machines (GBM)  0.281637             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.837240             NaN   
 28   Gradient Boosting Machines (GBM)  0.103147             NaN   
 29   Gradient Boosting Machines (GBM)  0.359756             NaN   
 30   Gradient Boosting Machines (GBM)  0.160326             NaN   
 31   Gradient Boosting Machines (GBM)  0.674597             NaN   
 32   Gradient Boosting Machines (GBM)  0.260738             NaN   
 33   Gradient Boosting Machines (GBM)  0.349194             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.833816             NaN   
 55   Gradient Boosting Machines (GBM)  0.106383             NaN   
 56   Gradient Boosting Machines (GBM)  0.320856             NaN   
 57   Gradient Boosting Machines (GBM)  0.159787             NaN   
 58   Gradient Boosting Machines (GBM)  0.670028             NaN   
 59   Gradient Boosting Machines (GBM)  0.253799             NaN   
 60   Gradient Boosting Machines (GBM)  0.340055             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.827713             NaN   
 82   Gradient Boosting Machines (GBM)  0.107877             NaN   
 83   Gradient Boosting Machines (GBM)  0.321429             NaN   
 84   Gradient Boosting Machines (GBM)  0.161538             NaN   
 85   Gradient Boosting Machines (GBM)  0.662846             NaN   
 86   Gradient Boosting Machines (GBM)  0.250867             NaN   
 87   Gradient Boosting Machines (GBM)  0.325692             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.825869             NaN   
 109  Gradient Boosting Machines (GBM)  0.111693             NaN   
 110  Gradient Boosting Machines (GBM)  0.296296             NaN   
 111  Gradient Boosting Machines (GBM)  0.162231             NaN   
 112  Gradient Boosting Machines (GBM)  0.670936             NaN   
 113  Gradient Boosting Machines (GBM)  0.285216             NaN   
 114  Gradient Boosting Machines (GBM)  0.341872             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique   Imbalance Class Technique  \
 0      Median Imputation  Hybrid Sampling (SMOTEENN)   
 1      Median Imputation  Hybrid Sampling (SMOTEENN)   
 2      Median Imputation  Hybrid Sampling (SMOTEENN)   
 3      Median Imputation  Hybrid Sampling (SMOTEENN)   
 4      Median Imputation  Hybrid Sampling (SMOTEENN)   
 5      Median Imputation  Hybrid Sampling (SMOTEENN)   
 6      Median Imputation  Hybrid Sampling (SMOTEENN)   
 7      Median Imputation  Hybrid Sampling (SMOTEENN)   
 8      Median Imputation  Hybrid Sampling (SMOTEENN)   
 9      Median Imputation  Hybrid Sampling (SMOTEENN)   
 10     Median Imputation  Hybrid Sampling (SMOTEENN)   
 11     Median Imputation  Hybrid Sampling (SMOTEENN)   
 12     Median Imputation  Hybrid Sampling (SMOTEENN)   
 13     Median Imputation  Hybrid Sampling (SMOTEENN)   
 14     Median Imputation  Hybrid Sampling (SMOTEENN)   
 15     Median Imputation  Hybrid Sampling (SMOTEENN)   
 16     Median Imputation  Hybrid Sampling (SMOTEENN)   
 17     Median Imputation  Hybrid Sampling (SMOTEENN)   
 18     Median Imputation  Hybrid Sampling (SMOTEENN)   
 19     Median Imputation  Hybrid Sampling (SMOTEENN)   
 20     Median Imputation  Hybrid Sampling (SMOTEENN)   
 21     Median Imputation  Hybrid Sampling (SMOTEENN)   
 22     Median Imputation  Hybrid Sampling (SMOTEENN)   
 23     Median Imputation  Hybrid Sampling (SMOTEENN)   
 24     Median Imputation  Hybrid Sampling (SMOTEENN)   
 25     Median Imputation  Hybrid Sampling (SMOTEENN)   
 26     Median Imputation  Hybrid Sampling (SMOTEENN)   
 27     Median Imputation  Hybrid Sampling (SMOTEENN)   
 28     Median Imputation  Hybrid Sampling (SMOTEENN)   
 29     Median Imputation  Hybrid Sampling (SMOTEENN)   
 30     Median Imputation  Hybrid Sampling (SMOTEENN)   
 31     Median Imputation  Hybrid Sampling (SMOTEENN)   
 32     Median Imputation  Hybrid Sampling (SMOTEENN)   
 33     Median Imputation  Hybrid Sampling (SMOTEENN)   
 34     Median Imputation  Hybrid Sampling (SMOTEENN)   
 35     Median Imputation  Hybrid Sampling (SMOTEENN)   
 36     Median Imputation  Hybrid Sampling (SMOTEENN)   
 37     Median Imputation  Hybrid Sampling (SMOTEENN)   
 38     Median Imputation  Hybrid Sampling (SMOTEENN)   
 39     Median Imputation  Hybrid Sampling (SMOTEENN)   
 40     Median Imputation  Hybrid Sampling (SMOTEENN)   
 41     Median Imputation  Hybrid Sampling (SMOTEENN)   
 42     Median Imputation  Hybrid Sampling (SMOTEENN)   
 43     Median Imputation  Hybrid Sampling (SMOTEENN)   
 44     Median Imputation  Hybrid Sampling (SMOTEENN)   
 45     Median Imputation  Hybrid Sampling (SMOTEENN)   
 46     Median Imputation  Hybrid Sampling (SMOTEENN)   
 47     Median Imputation  Hybrid Sampling (SMOTEENN)   
 48     Median Imputation  Hybrid Sampling (SMOTEENN)   
 49     Median Imputation  Hybrid Sampling (SMOTEENN)   
 50     Median Imputation  Hybrid Sampling (SMOTEENN)   
 51     Median Imputation  Hybrid Sampling (SMOTEENN)   
 52     Median Imputation  Hybrid Sampling (SMOTEENN)   
 53     Median Imputation  Hybrid Sampling (SMOTEENN)   
 54     Median Imputation  Hybrid Sampling (SMOTEENN)   
 55     Median Imputation  Hybrid Sampling (SMOTEENN)   
 56     Median Imputation  Hybrid Sampling (SMOTEENN)   
 57     Median Imputation  Hybrid Sampling (SMOTEENN)   
 58     Median Imputation  Hybrid Sampling (SMOTEENN)   
 59     Median Imputation  Hybrid Sampling (SMOTEENN)   
 60     Median Imputation  Hybrid Sampling (SMOTEENN)   
 61     Median Imputation  Hybrid Sampling (SMOTEENN)   
 62     Median Imputation  Hybrid Sampling (SMOTEENN)   
 63     Median Imputation  Hybrid Sampling (SMOTEENN)   
 64     Median Imputation  Hybrid Sampling (SMOTEENN)   
 65     Median Imputation  Hybrid Sampling (SMOTEENN)   
 66     Median Imputation  Hybrid Sampling (SMOTEENN)   
 67     Median Imputation  Hybrid Sampling (SMOTEENN)   
 68     Median Imputation  Hybrid Sampling (SMOTEENN)   
 69     Median Imputation  Hybrid Sampling (SMOTEENN)   
 70     Median Imputation  Hybrid Sampling (SMOTEENN)   
 71     Median Imputation  Hybrid Sampling (SMOTEENN)   
 72     Median Imputation  Hybrid Sampling (SMOTEENN)   
 73     Median Imputation  Hybrid Sampling (SMOTEENN)   
 74     Median Imputation  Hybrid Sampling (SMOTEENN)   
 75     Median Imputation  Hybrid Sampling (SMOTEENN)   
 76     Median Imputation  Hybrid Sampling (SMOTEENN)   
 77     Median Imputation  Hybrid Sampling (SMOTEENN)   
 78     Median Imputation  Hybrid Sampling (SMOTEENN)   
 79     Median Imputation  Hybrid Sampling (SMOTEENN)   
 80     Median Imputation  Hybrid Sampling (SMOTEENN)   
 81     Median Imputation  Hybrid Sampling (SMOTEENN)   
 82     Median Imputation  Hybrid Sampling (SMOTEENN)   
 83     Median Imputation  Hybrid Sampling (SMOTEENN)   
 84     Median Imputation  Hybrid Sampling (SMOTEENN)   
 85     Median Imputation  Hybrid Sampling (SMOTEENN)   
 86     Median Imputation  Hybrid Sampling (SMOTEENN)   
 87     Median Imputation  Hybrid Sampling (SMOTEENN)   
 88     Median Imputation  Hybrid Sampling (SMOTEENN)   
 89     Median Imputation  Hybrid Sampling (SMOTEENN)   
 90     Median Imputation  Hybrid Sampling (SMOTEENN)   
 91     Median Imputation  Hybrid Sampling (SMOTEENN)   
 92     Median Imputation  Hybrid Sampling (SMOTEENN)   
 93     Median Imputation  Hybrid Sampling (SMOTEENN)   
 94     Median Imputation  Hybrid Sampling (SMOTEENN)   
 95     Median Imputation  Hybrid Sampling (SMOTEENN)   
 96     Median Imputation  Hybrid Sampling (SMOTEENN)   
 97     Median Imputation  Hybrid Sampling (SMOTEENN)   
 98     Median Imputation  Hybrid Sampling (SMOTEENN)   
 99     Median Imputation  Hybrid Sampling (SMOTEENN)   
 100    Median Imputation  Hybrid Sampling (SMOTEENN)   
 101    Median Imputation  Hybrid Sampling (SMOTEENN)   
 102    Median Imputation  Hybrid Sampling (SMOTEENN)   
 103    Median Imputation  Hybrid Sampling (SMOTEENN)   
 104    Median Imputation  Hybrid Sampling (SMOTEENN)   
 105    Median Imputation  Hybrid Sampling (SMOTEENN)   
 106    Median Imputation  Hybrid Sampling (SMOTEENN)   
 107    Median Imputation  Hybrid Sampling (SMOTEENN)   
 108    Median Imputation  Hybrid Sampling (SMOTEENN)   
 109    Median Imputation  Hybrid Sampling (SMOTEENN)   
 110    Median Imputation  Hybrid Sampling (SMOTEENN)   
 111    Median Imputation  Hybrid Sampling (SMOTEENN)   
 112    Median Imputation  Hybrid Sampling (SMOTEENN)   
 113    Median Imputation  Hybrid Sampling (SMOTEENN)   
 114    Median Imputation  Hybrid Sampling (SMOTEENN)   
 115    Median Imputation  Hybrid Sampling (SMOTEENN)   
 116    Median Imputation  Hybrid Sampling (SMOTEENN)   
 117    Median Imputation  Hybrid Sampling (SMOTEENN)   
 118    Median Imputation  Hybrid Sampling (SMOTEENN)   
 119    Median Imputation  Hybrid Sampling (SMOTEENN)   
 120    Median Imputation  Hybrid Sampling (SMOTEENN)   
 121    Median Imputation  Hybrid Sampling (SMOTEENN)   
 122    Median Imputation  Hybrid Sampling (SMOTEENN)   
 123    Median Imputation  Hybrid Sampling (SMOTEENN)   
 124    Median Imputation  Hybrid Sampling (SMOTEENN)   
 125    Median Imputation  Hybrid Sampling (SMOTEENN)   
 126    Median Imputation  Hybrid Sampling (SMOTEENN)   
 127    Median Imputation  Hybrid Sampling (SMOTEENN)   
 128    Median Imputation  Hybrid Sampling (SMOTEENN)   
 129    Median Imputation  Hybrid Sampling (SMOTEENN)   
 130    Median Imputation  Hybrid Sampling (SMOTEENN)   
 131    Median Imputation  Hybrid Sampling (SMOTEENN)   
 132    Median Imputation  Hybrid Sampling (SMOTEENN)   
 133    Median Imputation  Hybrid Sampling (SMOTEENN)   
 134    Median Imputation  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.838030             NaN   
 1    Gradient Boosting Machines (GBM)  0.111111             NaN   
 2    Gradient Boosting Machines (GBM)  0.227848             NaN   
 3    Gradient Boosting Machines (GBM)  0.149378             NaN   
 4    Gradient Boosting Machines (GBM)  0.654424             NaN   
 5    Gradient Boosting Machines (GBM)  0.253470             NaN   
 6    Gradient Boosting Machines (GBM)  0.308849             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.852515             NaN   
 28   Gradient Boosting Machines (GBM)  0.108696             NaN   
 29   Gradient Boosting Machines (GBM)  0.335366             NaN   
 30   Gradient Boosting Machines (GBM)  0.164179             NaN   
 31   Gradient Boosting Machines (GBM)  0.698704             NaN   
 32   Gradient Boosting Machines (GBM)  0.306696             NaN   
 33   Gradient Boosting Machines (GBM)  0.397409             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.842244             NaN   
 55   Gradient Boosting Machines (GBM)  0.103846             NaN   
 56   Gradient Boosting Machines (GBM)  0.288770             NaN   
 57   Gradient Boosting Machines (GBM)  0.152758             NaN   
 58   Gradient Boosting Machines (GBM)  0.669725             NaN   
 59   Gradient Boosting Machines (GBM)  0.285719             NaN   
 60   Gradient Boosting Machines (GBM)  0.339451             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.851159             NaN   
 82   Gradient Boosting Machines (GBM)  0.125761             NaN   
 83   Gradient Boosting Machines (GBM)  0.316327             NaN   
 84   Gradient Boosting Machines (GBM)  0.179971             NaN   
 85   Gradient Boosting Machines (GBM)  0.679990             NaN   
 86   Gradient Boosting Machines (GBM)  0.297358             NaN   
 87   Gradient Boosting Machines (GBM)  0.359980             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.855901             NaN   
 109  Gradient Boosting Machines (GBM)  0.139434             NaN   
 110  Gradient Boosting Machines (GBM)  0.296296             NaN   
 111  Gradient Boosting Machines (GBM)  0.189630             NaN   
 112  Gradient Boosting Machines (GBM)  0.683147             NaN   
 113  Gradient Boosting Machines (GBM)  0.291118             NaN   
 114  Gradient Boosting Machines (GBM)  0.366294             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 1        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 2        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 3        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 4        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 5        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 6        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 7        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 8        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 9        Zero Imputation  Hybrid Sampling (SMOTETomek)   
 10       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 11       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 12       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 13       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 14       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 15       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 16       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 17       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 18       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 19       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 20       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 21       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 22       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 23       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 24       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 25       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 26       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 27       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 28       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 29       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 30       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 31       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 32       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 33       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 34       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 35       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 36       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 37       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 38       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 39       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 40       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 41       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 42       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 43       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 44       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 45       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 46       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 47       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 48       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 49       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 50       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 51       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 52       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 53       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 54       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 55       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 56       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 57       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 58       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 59       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 60       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 61       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 62       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 63       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 64       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 65       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 66       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 67       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 68       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 69       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 70       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 71       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 72       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 73       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 74       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 75       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 76       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 77       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 78       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 79       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 80       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 81       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 82       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 83       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 84       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 85       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 86       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 87       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 88       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 89       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 90       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 91       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 92       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 93       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 94       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 95       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 96       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 97       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 98       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 99       Zero Imputation  Hybrid Sampling (SMOTETomek)   
 100      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 101      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 102      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 103      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 104      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 105      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 106      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 107      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 108      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 109      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 110      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 111      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 112      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 113      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 114      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 115      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 116      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 117      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 118      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 119      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 120      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 121      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 122      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 123      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 124      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 125      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 126      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 127      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 128      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 129      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 130      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 131      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 132      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 133      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 134      Zero Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.840664             NaN   
 1    Gradient Boosting Machines (GBM)  0.119835             NaN   
 2    Gradient Boosting Machines (GBM)  0.244726             NaN   
 3    Gradient Boosting Machines (GBM)  0.160888             NaN   
 4    Gradient Boosting Machines (GBM)  0.655089             NaN   
 5    Gradient Boosting Machines (GBM)  0.241046             NaN   
 6    Gradient Boosting Machines (GBM)  0.310178             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.846721             NaN   
 28   Gradient Boosting Machines (GBM)  0.108614             NaN   
 29   Gradient Boosting Machines (GBM)  0.353659             NaN   
 30   Gradient Boosting Machines (GBM)  0.166189             NaN   
 31   Gradient Boosting Machines (GBM)  0.670539             NaN   
 32   Gradient Boosting Machines (GBM)  0.271314             NaN   
 33   Gradient Boosting Machines (GBM)  0.341077             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.845931             NaN   
 55   Gradient Boosting Machines (GBM)  0.123106             NaN   
 56   Gradient Boosting Machines (GBM)  0.347594             NaN   
 57   Gradient Boosting Machines (GBM)  0.181818             NaN   
 58   Gradient Boosting Machines (GBM)  0.673389             NaN   
 59   Gradient Boosting Machines (GBM)  0.262072             NaN   
 60   Gradient Boosting Machines (GBM)  0.346779             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.842466             NaN   
 82   Gradient Boosting Machines (GBM)  0.119318             NaN   
 83   Gradient Boosting Machines (GBM)  0.321429             NaN   
 84   Gradient Boosting Machines (GBM)  0.174033             NaN   
 85   Gradient Boosting Machines (GBM)  0.675806             NaN   
 86   Gradient Boosting Machines (GBM)  0.304104             NaN   
 87   Gradient Boosting Machines (GBM)  0.351613             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.843783             NaN   
 109  Gradient Boosting Machines (GBM)  0.126733             NaN   
 110  Gradient Boosting Machines (GBM)  0.296296             NaN   
 111  Gradient Boosting Machines (GBM)  0.177531             NaN   
 112  Gradient Boosting Machines (GBM)  0.684996             NaN   
 113  Gradient Boosting Machines (GBM)  0.306145             NaN   
 114  Gradient Boosting Machines (GBM)  0.369991             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 1        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 2        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 3        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 4        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 5        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 6        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 7        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 8        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 9        Mean Imputation  Hybrid Sampling (SMOTETomek)   
 10       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 11       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 12       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 13       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 14       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 15       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 16       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 17       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 18       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 19       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 20       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 21       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 22       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 23       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 24       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 25       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 26       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 27       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 28       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 29       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 30       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 31       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 32       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 33       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 34       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 35       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 36       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 37       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 38       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 39       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 40       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 41       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 42       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 43       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 44       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 45       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 46       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 47       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 48       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 49       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 50       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 51       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 52       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 53       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 54       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 55       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 56       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 57       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 58       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 59       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 60       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 61       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 62       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 63       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 64       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 65       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 66       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 67       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 68       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 69       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 70       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 71       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 72       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 73       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 74       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 75       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 76       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 77       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 78       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 79       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 80       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 81       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 82       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 83       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 84       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 85       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 86       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 87       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 88       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 89       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 90       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 91       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 92       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 93       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 94       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 95       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 96       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 97       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 98       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 99       Mean Imputation  Hybrid Sampling (SMOTETomek)   
 100      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 101      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 102      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 103      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 104      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 105      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 106      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 107      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 108      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 109      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 110      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 111      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 112      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 113      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 114      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 115      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 116      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 117      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 118      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 119      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 120      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 121      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 122      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 123      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 124      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 125      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 126      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 127      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 128      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 129      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 130      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 131      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 132      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 133      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 134      Mean Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
                             Algorithm   Metrics Hyperparameters  \
 0    Gradient Boosting Machines (GBM)  0.836977             NaN   
 1    Gradient Boosting Machines (GBM)  0.106996             NaN   
 2    Gradient Boosting Machines (GBM)  0.219409             NaN   
 3    Gradient Boosting Machines (GBM)  0.143845             NaN   
 4    Gradient Boosting Machines (GBM)  0.634986             NaN   
 5    Gradient Boosting Machines (GBM)  0.202133             NaN   
 6    Gradient Boosting Machines (GBM)  0.269971             NaN   
 7    Gradient Boosting Machines (GBM)       NaN             0.0   
 8    Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 9    Gradient Boosting Machines (GBM)       NaN            None   
 10   Gradient Boosting Machines (GBM)       NaN             0.1   
 11   Gradient Boosting Machines (GBM)       NaN        log_loss   
 12   Gradient Boosting Machines (GBM)       NaN               3   
 13   Gradient Boosting Machines (GBM)       NaN            None   
 14   Gradient Boosting Machines (GBM)       NaN            None   
 15   Gradient Boosting Machines (GBM)       NaN             0.0   
 16   Gradient Boosting Machines (GBM)       NaN               1   
 17   Gradient Boosting Machines (GBM)       NaN               2   
 18   Gradient Boosting Machines (GBM)       NaN             0.0   
 19   Gradient Boosting Machines (GBM)       NaN             100   
 20   Gradient Boosting Machines (GBM)       NaN            None   
 21   Gradient Boosting Machines (GBM)       NaN            None   
 22   Gradient Boosting Machines (GBM)       NaN             1.0   
 23   Gradient Boosting Machines (GBM)       NaN          0.0001   
 24   Gradient Boosting Machines (GBM)       NaN             0.1   
 25   Gradient Boosting Machines (GBM)       NaN               0   
 26   Gradient Boosting Machines (GBM)       NaN           False   
 27   Gradient Boosting Machines (GBM)  0.855412             NaN   
 28   Gradient Boosting Machines (GBM)  0.104723             NaN   
 29   Gradient Boosting Machines (GBM)  0.310976             NaN   
 30   Gradient Boosting Machines (GBM)  0.156682             NaN   
 31   Gradient Boosting Machines (GBM)  0.667502             NaN   
 32   Gradient Boosting Machines (GBM)  0.257262             NaN   
 33   Gradient Boosting Machines (GBM)  0.335003             NaN   
 34   Gradient Boosting Machines (GBM)       NaN             0.0   
 35   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 36   Gradient Boosting Machines (GBM)       NaN            None   
 37   Gradient Boosting Machines (GBM)       NaN             0.1   
 38   Gradient Boosting Machines (GBM)       NaN        log_loss   
 39   Gradient Boosting Machines (GBM)       NaN               3   
 40   Gradient Boosting Machines (GBM)       NaN            None   
 41   Gradient Boosting Machines (GBM)       NaN            None   
 42   Gradient Boosting Machines (GBM)       NaN             0.0   
 43   Gradient Boosting Machines (GBM)       NaN               1   
 44   Gradient Boosting Machines (GBM)       NaN               2   
 45   Gradient Boosting Machines (GBM)       NaN             0.0   
 46   Gradient Boosting Machines (GBM)       NaN             100   
 47   Gradient Boosting Machines (GBM)       NaN            None   
 48   Gradient Boosting Machines (GBM)       NaN            None   
 49   Gradient Boosting Machines (GBM)       NaN             1.0   
 50   Gradient Boosting Machines (GBM)       NaN          0.0001   
 51   Gradient Boosting Machines (GBM)       NaN             0.1   
 52   Gradient Boosting Machines (GBM)       NaN               0   
 53   Gradient Boosting Machines (GBM)       NaN           False   
 54   Gradient Boosting Machines (GBM)  0.849881             NaN   
 55   Gradient Boosting Machines (GBM)  0.116232             NaN   
 56   Gradient Boosting Machines (GBM)  0.310160             NaN   
 57   Gradient Boosting Machines (GBM)  0.169096             NaN   
 58   Gradient Boosting Machines (GBM)  0.673500             NaN   
 59   Gradient Boosting Machines (GBM)  0.260268             NaN   
 60   Gradient Boosting Machines (GBM)  0.347000             NaN   
 61   Gradient Boosting Machines (GBM)       NaN             0.0   
 62   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 63   Gradient Boosting Machines (GBM)       NaN            None   
 64   Gradient Boosting Machines (GBM)       NaN             0.1   
 65   Gradient Boosting Machines (GBM)       NaN        log_loss   
 66   Gradient Boosting Machines (GBM)       NaN               3   
 67   Gradient Boosting Machines (GBM)       NaN            None   
 68   Gradient Boosting Machines (GBM)       NaN            None   
 69   Gradient Boosting Machines (GBM)       NaN             0.0   
 70   Gradient Boosting Machines (GBM)       NaN               1   
 71   Gradient Boosting Machines (GBM)       NaN               2   
 72   Gradient Boosting Machines (GBM)       NaN             0.0   
 73   Gradient Boosting Machines (GBM)       NaN             100   
 74   Gradient Boosting Machines (GBM)       NaN            None   
 75   Gradient Boosting Machines (GBM)       NaN            None   
 76   Gradient Boosting Machines (GBM)       NaN             1.0   
 77   Gradient Boosting Machines (GBM)       NaN          0.0001   
 78   Gradient Boosting Machines (GBM)       NaN             0.1   
 79   Gradient Boosting Machines (GBM)       NaN               0   
 80   Gradient Boosting Machines (GBM)       NaN           False   
 81   Gradient Boosting Machines (GBM)  0.844310             NaN   
 82   Gradient Boosting Machines (GBM)  0.104208             NaN   
 83   Gradient Boosting Machines (GBM)  0.265306             NaN   
 84   Gradient Boosting Machines (GBM)  0.149640             NaN   
 85   Gradient Boosting Machines (GBM)  0.659612             NaN   
 86   Gradient Boosting Machines (GBM)  0.247545             NaN   
 87   Gradient Boosting Machines (GBM)  0.319225             NaN   
 88   Gradient Boosting Machines (GBM)       NaN             0.0   
 89   Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 90   Gradient Boosting Machines (GBM)       NaN            None   
 91   Gradient Boosting Machines (GBM)       NaN             0.1   
 92   Gradient Boosting Machines (GBM)       NaN        log_loss   
 93   Gradient Boosting Machines (GBM)       NaN               3   
 94   Gradient Boosting Machines (GBM)       NaN            None   
 95   Gradient Boosting Machines (GBM)       NaN            None   
 96   Gradient Boosting Machines (GBM)       NaN             0.0   
 97   Gradient Boosting Machines (GBM)       NaN               1   
 98   Gradient Boosting Machines (GBM)       NaN               2   
 99   Gradient Boosting Machines (GBM)       NaN             0.0   
 100  Gradient Boosting Machines (GBM)       NaN             100   
 101  Gradient Boosting Machines (GBM)       NaN            None   
 102  Gradient Boosting Machines (GBM)       NaN            None   
 103  Gradient Boosting Machines (GBM)       NaN             1.0   
 104  Gradient Boosting Machines (GBM)       NaN          0.0001   
 105  Gradient Boosting Machines (GBM)       NaN             0.1   
 106  Gradient Boosting Machines (GBM)       NaN               0   
 107  Gradient Boosting Machines (GBM)       NaN           False   
 108  Gradient Boosting Machines (GBM)  0.850632             NaN   
 109  Gradient Boosting Machines (GBM)  0.136646             NaN   
 110  Gradient Boosting Machines (GBM)  0.305556             NaN   
 111  Gradient Boosting Machines (GBM)  0.188841             NaN   
 112  Gradient Boosting Machines (GBM)  0.675818             NaN   
 113  Gradient Boosting Machines (GBM)  0.283152             NaN   
 114  Gradient Boosting Machines (GBM)  0.351636             NaN   
 115  Gradient Boosting Machines (GBM)       NaN             0.0   
 116  Gradient Boosting Machines (GBM)       NaN    friedman_mse   
 117  Gradient Boosting Machines (GBM)       NaN            None   
 118  Gradient Boosting Machines (GBM)       NaN             0.1   
 119  Gradient Boosting Machines (GBM)       NaN        log_loss   
 120  Gradient Boosting Machines (GBM)       NaN               3   
 121  Gradient Boosting Machines (GBM)       NaN            None   
 122  Gradient Boosting Machines (GBM)       NaN            None   
 123  Gradient Boosting Machines (GBM)       NaN             0.0   
 124  Gradient Boosting Machines (GBM)       NaN               1   
 125  Gradient Boosting Machines (GBM)       NaN               2   
 126  Gradient Boosting Machines (GBM)       NaN             0.0   
 127  Gradient Boosting Machines (GBM)       NaN             100   
 128  Gradient Boosting Machines (GBM)       NaN            None   
 129  Gradient Boosting Machines (GBM)       NaN            None   
 130  Gradient Boosting Machines (GBM)       NaN             1.0   
 131  Gradient Boosting Machines (GBM)       NaN          0.0001   
 132  Gradient Boosting Machines (GBM)       NaN             0.1   
 133  Gradient Boosting Machines (GBM)       NaN               0   
 134  Gradient Boosting Machines (GBM)       NaN           False   
 
     Imputation Technique     Imbalance Class Technique  \
 0      Median Imputation  Hybrid Sampling (SMOTETomek)   
 1      Median Imputation  Hybrid Sampling (SMOTETomek)   
 2      Median Imputation  Hybrid Sampling (SMOTETomek)   
 3      Median Imputation  Hybrid Sampling (SMOTETomek)   
 4      Median Imputation  Hybrid Sampling (SMOTETomek)   
 5      Median Imputation  Hybrid Sampling (SMOTETomek)   
 6      Median Imputation  Hybrid Sampling (SMOTETomek)   
 7      Median Imputation  Hybrid Sampling (SMOTETomek)   
 8      Median Imputation  Hybrid Sampling (SMOTETomek)   
 9      Median Imputation  Hybrid Sampling (SMOTETomek)   
 10     Median Imputation  Hybrid Sampling (SMOTETomek)   
 11     Median Imputation  Hybrid Sampling (SMOTETomek)   
 12     Median Imputation  Hybrid Sampling (SMOTETomek)   
 13     Median Imputation  Hybrid Sampling (SMOTETomek)   
 14     Median Imputation  Hybrid Sampling (SMOTETomek)   
 15     Median Imputation  Hybrid Sampling (SMOTETomek)   
 16     Median Imputation  Hybrid Sampling (SMOTETomek)   
 17     Median Imputation  Hybrid Sampling (SMOTETomek)   
 18     Median Imputation  Hybrid Sampling (SMOTETomek)   
 19     Median Imputation  Hybrid Sampling (SMOTETomek)   
 20     Median Imputation  Hybrid Sampling (SMOTETomek)   
 21     Median Imputation  Hybrid Sampling (SMOTETomek)   
 22     Median Imputation  Hybrid Sampling (SMOTETomek)   
 23     Median Imputation  Hybrid Sampling (SMOTETomek)   
 24     Median Imputation  Hybrid Sampling (SMOTETomek)   
 25     Median Imputation  Hybrid Sampling (SMOTETomek)   
 26     Median Imputation  Hybrid Sampling (SMOTETomek)   
 27     Median Imputation  Hybrid Sampling (SMOTETomek)   
 28     Median Imputation  Hybrid Sampling (SMOTETomek)   
 29     Median Imputation  Hybrid Sampling (SMOTETomek)   
 30     Median Imputation  Hybrid Sampling (SMOTETomek)   
 31     Median Imputation  Hybrid Sampling (SMOTETomek)   
 32     Median Imputation  Hybrid Sampling (SMOTETomek)   
 33     Median Imputation  Hybrid Sampling (SMOTETomek)   
 34     Median Imputation  Hybrid Sampling (SMOTETomek)   
 35     Median Imputation  Hybrid Sampling (SMOTETomek)   
 36     Median Imputation  Hybrid Sampling (SMOTETomek)   
 37     Median Imputation  Hybrid Sampling (SMOTETomek)   
 38     Median Imputation  Hybrid Sampling (SMOTETomek)   
 39     Median Imputation  Hybrid Sampling (SMOTETomek)   
 40     Median Imputation  Hybrid Sampling (SMOTETomek)   
 41     Median Imputation  Hybrid Sampling (SMOTETomek)   
 42     Median Imputation  Hybrid Sampling (SMOTETomek)   
 43     Median Imputation  Hybrid Sampling (SMOTETomek)   
 44     Median Imputation  Hybrid Sampling (SMOTETomek)   
 45     Median Imputation  Hybrid Sampling (SMOTETomek)   
 46     Median Imputation  Hybrid Sampling (SMOTETomek)   
 47     Median Imputation  Hybrid Sampling (SMOTETomek)   
 48     Median Imputation  Hybrid Sampling (SMOTETomek)   
 49     Median Imputation  Hybrid Sampling (SMOTETomek)   
 50     Median Imputation  Hybrid Sampling (SMOTETomek)   
 51     Median Imputation  Hybrid Sampling (SMOTETomek)   
 52     Median Imputation  Hybrid Sampling (SMOTETomek)   
 53     Median Imputation  Hybrid Sampling (SMOTETomek)   
 54     Median Imputation  Hybrid Sampling (SMOTETomek)   
 55     Median Imputation  Hybrid Sampling (SMOTETomek)   
 56     Median Imputation  Hybrid Sampling (SMOTETomek)   
 57     Median Imputation  Hybrid Sampling (SMOTETomek)   
 58     Median Imputation  Hybrid Sampling (SMOTETomek)   
 59     Median Imputation  Hybrid Sampling (SMOTETomek)   
 60     Median Imputation  Hybrid Sampling (SMOTETomek)   
 61     Median Imputation  Hybrid Sampling (SMOTETomek)   
 62     Median Imputation  Hybrid Sampling (SMOTETomek)   
 63     Median Imputation  Hybrid Sampling (SMOTETomek)   
 64     Median Imputation  Hybrid Sampling (SMOTETomek)   
 65     Median Imputation  Hybrid Sampling (SMOTETomek)   
 66     Median Imputation  Hybrid Sampling (SMOTETomek)   
 67     Median Imputation  Hybrid Sampling (SMOTETomek)   
 68     Median Imputation  Hybrid Sampling (SMOTETomek)   
 69     Median Imputation  Hybrid Sampling (SMOTETomek)   
 70     Median Imputation  Hybrid Sampling (SMOTETomek)   
 71     Median Imputation  Hybrid Sampling (SMOTETomek)   
 72     Median Imputation  Hybrid Sampling (SMOTETomek)   
 73     Median Imputation  Hybrid Sampling (SMOTETomek)   
 74     Median Imputation  Hybrid Sampling (SMOTETomek)   
 75     Median Imputation  Hybrid Sampling (SMOTETomek)   
 76     Median Imputation  Hybrid Sampling (SMOTETomek)   
 77     Median Imputation  Hybrid Sampling (SMOTETomek)   
 78     Median Imputation  Hybrid Sampling (SMOTETomek)   
 79     Median Imputation  Hybrid Sampling (SMOTETomek)   
 80     Median Imputation  Hybrid Sampling (SMOTETomek)   
 81     Median Imputation  Hybrid Sampling (SMOTETomek)   
 82     Median Imputation  Hybrid Sampling (SMOTETomek)   
 83     Median Imputation  Hybrid Sampling (SMOTETomek)   
 84     Median Imputation  Hybrid Sampling (SMOTETomek)   
 85     Median Imputation  Hybrid Sampling (SMOTETomek)   
 86     Median Imputation  Hybrid Sampling (SMOTETomek)   
 87     Median Imputation  Hybrid Sampling (SMOTETomek)   
 88     Median Imputation  Hybrid Sampling (SMOTETomek)   
 89     Median Imputation  Hybrid Sampling (SMOTETomek)   
 90     Median Imputation  Hybrid Sampling (SMOTETomek)   
 91     Median Imputation  Hybrid Sampling (SMOTETomek)   
 92     Median Imputation  Hybrid Sampling (SMOTETomek)   
 93     Median Imputation  Hybrid Sampling (SMOTETomek)   
 94     Median Imputation  Hybrid Sampling (SMOTETomek)   
 95     Median Imputation  Hybrid Sampling (SMOTETomek)   
 96     Median Imputation  Hybrid Sampling (SMOTETomek)   
 97     Median Imputation  Hybrid Sampling (SMOTETomek)   
 98     Median Imputation  Hybrid Sampling (SMOTETomek)   
 99     Median Imputation  Hybrid Sampling (SMOTETomek)   
 100    Median Imputation  Hybrid Sampling (SMOTETomek)   
 101    Median Imputation  Hybrid Sampling (SMOTETomek)   
 102    Median Imputation  Hybrid Sampling (SMOTETomek)   
 103    Median Imputation  Hybrid Sampling (SMOTETomek)   
 104    Median Imputation  Hybrid Sampling (SMOTETomek)   
 105    Median Imputation  Hybrid Sampling (SMOTETomek)   
 106    Median Imputation  Hybrid Sampling (SMOTETomek)   
 107    Median Imputation  Hybrid Sampling (SMOTETomek)   
 108    Median Imputation  Hybrid Sampling (SMOTETomek)   
 109    Median Imputation  Hybrid Sampling (SMOTETomek)   
 110    Median Imputation  Hybrid Sampling (SMOTETomek)   
 111    Median Imputation  Hybrid Sampling (SMOTETomek)   
 112    Median Imputation  Hybrid Sampling (SMOTETomek)   
 113    Median Imputation  Hybrid Sampling (SMOTETomek)   
 114    Median Imputation  Hybrid Sampling (SMOTETomek)   
 115    Median Imputation  Hybrid Sampling (SMOTETomek)   
 116    Median Imputation  Hybrid Sampling (SMOTETomek)   
 117    Median Imputation  Hybrid Sampling (SMOTETomek)   
 118    Median Imputation  Hybrid Sampling (SMOTETomek)   
 119    Median Imputation  Hybrid Sampling (SMOTETomek)   
 120    Median Imputation  Hybrid Sampling (SMOTETomek)   
 121    Median Imputation  Hybrid Sampling (SMOTETomek)   
 122    Median Imputation  Hybrid Sampling (SMOTETomek)   
 123    Median Imputation  Hybrid Sampling (SMOTETomek)   
 124    Median Imputation  Hybrid Sampling (SMOTETomek)   
 125    Median Imputation  Hybrid Sampling (SMOTETomek)   
 126    Median Imputation  Hybrid Sampling (SMOTETomek)   
 127    Median Imputation  Hybrid Sampling (SMOTETomek)   
 128    Median Imputation  Hybrid Sampling (SMOTETomek)   
 129    Median Imputation  Hybrid Sampling (SMOTETomek)   
 130    Median Imputation  Hybrid Sampling (SMOTETomek)   
 131    Median Imputation  Hybrid Sampling (SMOTETomek)   
 132    Median Imputation  Hybrid Sampling (SMOTETomek)   
 133    Median Imputation  Hybrid Sampling (SMOTETomek)   
 134    Median Imputation  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 1    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 2    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 3    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 4    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 5    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 6    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 7    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 8    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 9    Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 10   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 11   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 12   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 13   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 14   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 15   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 16   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 17   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 18   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 19   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 20   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 21   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 22   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 23   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 24   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 25   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 26   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 27   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 28   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 29   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 30   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 31   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 32   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 33   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 34   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 35   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 36   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 37   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 38   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 39   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 40   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 41   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 42   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 43   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 44   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 45   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 46   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 47   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 48   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 49   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 50   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 51   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 52   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 53   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 54   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 55   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 56   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 57   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 58   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 59   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 60   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 61   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 62   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 63   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 64   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 65   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 66   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 67   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 68   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 69   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 70   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 71   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 72   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 73   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 74   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 75   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 76   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 77   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 78   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 79   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 80   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 81   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 82   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 83   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 84   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 85   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 86   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 87   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 88   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 89   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 90   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 91   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 92   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 93   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 94   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 95   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 96   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 97   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 98   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 99   Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 100  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 101  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 102  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 103  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 104  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 105  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 106  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 107  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 108  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 109  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 110  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 111  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 112  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 113  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 114  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 115  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 116  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 117  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 118  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 119  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 120  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 121  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 122  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 123  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 124  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 125  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 126  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 127  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 128  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 129  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 130  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 131  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 132  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 133  Gradient Boosting Machines (GBM)_Hybrid Sampli...  
 134  Gradient Boosting Machines (GBM)_Hybrid Sampli...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.842771              NaN      Zero Imputation   
 1     XGBoost  0.134146              NaN      Zero Imputation   
 2     XGBoost  0.278481              NaN      Zero Imputation   
 3     XGBoost  0.181070              NaN      Zero Imputation   
 4     XGBoost  0.659895              NaN      Zero Imputation   
 5     XGBoost  0.243817              NaN      Zero Imputation   
 6     XGBoost  0.319791              NaN      Zero Imputation   
 7     XGBoost       NaN  binary:logistic      Zero Imputation   
 8     XGBoost       NaN             None      Zero Imputation   
 9     XGBoost       NaN             None      Zero Imputation   
 10    XGBoost       NaN             None      Zero Imputation   
 11    XGBoost       NaN             None      Zero Imputation   
 12    XGBoost       NaN             None      Zero Imputation   
 13    XGBoost       NaN             None      Zero Imputation   
 14    XGBoost       NaN             None      Zero Imputation   
 15    XGBoost       NaN             None      Zero Imputation   
 16    XGBoost       NaN            False      Zero Imputation   
 17    XGBoost       NaN             None      Zero Imputation   
 18    XGBoost       NaN             None      Zero Imputation   
 19    XGBoost       NaN             None      Zero Imputation   
 20    XGBoost       NaN             None      Zero Imputation   
 21    XGBoost       NaN             None      Zero Imputation   
 22    XGBoost       NaN             None      Zero Imputation   
 23    XGBoost       NaN             None      Zero Imputation   
 24    XGBoost       NaN             None      Zero Imputation   
 25    XGBoost       NaN             None      Zero Imputation   
 26    XGBoost       NaN             None      Zero Imputation   
 27    XGBoost       NaN             None      Zero Imputation   
 28    XGBoost       NaN             None      Zero Imputation   
 29    XGBoost       NaN             None      Zero Imputation   
 30    XGBoost       NaN             None      Zero Imputation   
 31    XGBoost       NaN              NaN      Zero Imputation   
 32    XGBoost       NaN             None      Zero Imputation   
 33    XGBoost       NaN             None      Zero Imputation   
 34    XGBoost       NaN             None      Zero Imputation   
 35    XGBoost       NaN             None      Zero Imputation   
 36    XGBoost       NaN             None      Zero Imputation   
 37    XGBoost       NaN             None      Zero Imputation   
 38    XGBoost       NaN             None      Zero Imputation   
 39    XGBoost       NaN             None      Zero Imputation   
 40    XGBoost       NaN             None      Zero Imputation   
 41    XGBoost       NaN             None      Zero Imputation   
 42    XGBoost       NaN             None      Zero Imputation   
 43    XGBoost       NaN             None      Zero Imputation   
 44    XGBoost       NaN             None      Zero Imputation   
 45    XGBoost       NaN             None      Zero Imputation   
 46    XGBoost  0.845931              NaN      Zero Imputation   
 47    XGBoost  0.103578              NaN      Zero Imputation   
 48    XGBoost  0.335366              NaN      Zero Imputation   
 49    XGBoost  0.158273              NaN      Zero Imputation   
 50    XGBoost  0.684136              NaN      Zero Imputation   
 51    XGBoost  0.282450              NaN      Zero Imputation   
 52    XGBoost  0.368272              NaN      Zero Imputation   
 53    XGBoost       NaN  binary:logistic      Zero Imputation   
 54    XGBoost       NaN             None      Zero Imputation   
 55    XGBoost       NaN             None      Zero Imputation   
 56    XGBoost       NaN             None      Zero Imputation   
 57    XGBoost       NaN             None      Zero Imputation   
 58    XGBoost       NaN             None      Zero Imputation   
 59    XGBoost       NaN             None      Zero Imputation   
 60    XGBoost       NaN             None      Zero Imputation   
 61    XGBoost       NaN             None      Zero Imputation   
 62    XGBoost       NaN            False      Zero Imputation   
 63    XGBoost       NaN             None      Zero Imputation   
 64    XGBoost       NaN             None      Zero Imputation   
 65    XGBoost       NaN             None      Zero Imputation   
 66    XGBoost       NaN             None      Zero Imputation   
 67    XGBoost       NaN             None      Zero Imputation   
 68    XGBoost       NaN             None      Zero Imputation   
 69    XGBoost       NaN             None      Zero Imputation   
 70    XGBoost       NaN             None      Zero Imputation   
 71    XGBoost       NaN             None      Zero Imputation   
 72    XGBoost       NaN             None      Zero Imputation   
 73    XGBoost       NaN             None      Zero Imputation   
 74    XGBoost       NaN             None      Zero Imputation   
 75    XGBoost       NaN             None      Zero Imputation   
 76    XGBoost       NaN             None      Zero Imputation   
 77    XGBoost       NaN              NaN      Zero Imputation   
 78    XGBoost       NaN             None      Zero Imputation   
 79    XGBoost       NaN             None      Zero Imputation   
 80    XGBoost       NaN             None      Zero Imputation   
 81    XGBoost       NaN             None      Zero Imputation   
 82    XGBoost       NaN             None      Zero Imputation   
 83    XGBoost       NaN             None      Zero Imputation   
 84    XGBoost       NaN             None      Zero Imputation   
 85    XGBoost       NaN             None      Zero Imputation   
 86    XGBoost       NaN             None      Zero Imputation   
 87    XGBoost       NaN             None      Zero Imputation   
 88    XGBoost       NaN             None      Zero Imputation   
 89    XGBoost       NaN             None      Zero Imputation   
 90    XGBoost       NaN             None      Zero Imputation   
 91    XGBoost       NaN             None      Zero Imputation   
 92    XGBoost  0.842507              NaN      Zero Imputation   
 93    XGBoost  0.117318              NaN      Zero Imputation   
 94    XGBoost  0.336898              NaN      Zero Imputation   
 95    XGBoost  0.174033              NaN      Zero Imputation   
 96    XGBoost  0.672556              NaN      Zero Imputation   
 97    XGBoost  0.280732              NaN      Zero Imputation   
 98    XGBoost  0.345112              NaN      Zero Imputation   
 99    XGBoost       NaN  binary:logistic      Zero Imputation   
 100   XGBoost       NaN             None      Zero Imputation   
 101   XGBoost       NaN             None      Zero Imputation   
 102   XGBoost       NaN             None      Zero Imputation   
 103   XGBoost       NaN             None      Zero Imputation   
 104   XGBoost       NaN             None      Zero Imputation   
 105   XGBoost       NaN             None      Zero Imputation   
 106   XGBoost       NaN             None      Zero Imputation   
 107   XGBoost       NaN             None      Zero Imputation   
 108   XGBoost       NaN            False      Zero Imputation   
 109   XGBoost       NaN             None      Zero Imputation   
 110   XGBoost       NaN             None      Zero Imputation   
 111   XGBoost       NaN             None      Zero Imputation   
 112   XGBoost       NaN             None      Zero Imputation   
 113   XGBoost       NaN             None      Zero Imputation   
 114   XGBoost       NaN             None      Zero Imputation   
 115   XGBoost       NaN             None      Zero Imputation   
 116   XGBoost       NaN             None      Zero Imputation   
 117   XGBoost       NaN             None      Zero Imputation   
 118   XGBoost       NaN             None      Zero Imputation   
 119   XGBoost       NaN             None      Zero Imputation   
 120   XGBoost       NaN             None      Zero Imputation   
 121   XGBoost       NaN             None      Zero Imputation   
 122   XGBoost       NaN             None      Zero Imputation   
 123   XGBoost       NaN              NaN      Zero Imputation   
 124   XGBoost       NaN             None      Zero Imputation   
 125   XGBoost       NaN             None      Zero Imputation   
 126   XGBoost       NaN             None      Zero Imputation   
 127   XGBoost       NaN             None      Zero Imputation   
 128   XGBoost       NaN             None      Zero Imputation   
 129   XGBoost       NaN             None      Zero Imputation   
 130   XGBoost       NaN             None      Zero Imputation   
 131   XGBoost       NaN             None      Zero Imputation   
 132   XGBoost       NaN             None      Zero Imputation   
 133   XGBoost       NaN             None      Zero Imputation   
 134   XGBoost       NaN             None      Zero Imputation   
 135   XGBoost       NaN             None      Zero Imputation   
 136   XGBoost       NaN             None      Zero Imputation   
 137   XGBoost       NaN             None      Zero Imputation   
 138   XGBoost  0.846944              NaN      Zero Imputation   
 139   XGBoost  0.117296              NaN      Zero Imputation   
 140   XGBoost  0.301020              NaN      Zero Imputation   
 141   XGBoost  0.168813              NaN      Zero Imputation   
 142   XGBoost  0.668695              NaN      Zero Imputation   
 143   XGBoost  0.254439              NaN      Zero Imputation   
 144   XGBoost  0.337389              NaN      Zero Imputation   
 145   XGBoost       NaN  binary:logistic      Zero Imputation   
 146   XGBoost       NaN             None      Zero Imputation   
 147   XGBoost       NaN             None      Zero Imputation   
 148   XGBoost       NaN             None      Zero Imputation   
 149   XGBoost       NaN             None      Zero Imputation   
 150   XGBoost       NaN             None      Zero Imputation   
 151   XGBoost       NaN             None      Zero Imputation   
 152   XGBoost       NaN             None      Zero Imputation   
 153   XGBoost       NaN             None      Zero Imputation   
 154   XGBoost       NaN            False      Zero Imputation   
 155   XGBoost       NaN             None      Zero Imputation   
 156   XGBoost       NaN             None      Zero Imputation   
 157   XGBoost       NaN             None      Zero Imputation   
 158   XGBoost       NaN             None      Zero Imputation   
 159   XGBoost       NaN             None      Zero Imputation   
 160   XGBoost       NaN             None      Zero Imputation   
 161   XGBoost       NaN             None      Zero Imputation   
 162   XGBoost       NaN             None      Zero Imputation   
 163   XGBoost       NaN             None      Zero Imputation   
 164   XGBoost       NaN             None      Zero Imputation   
 165   XGBoost       NaN             None      Zero Imputation   
 166   XGBoost       NaN             None      Zero Imputation   
 167   XGBoost       NaN             None      Zero Imputation   
 168   XGBoost       NaN             None      Zero Imputation   
 169   XGBoost       NaN              NaN      Zero Imputation   
 170   XGBoost       NaN             None      Zero Imputation   
 171   XGBoost       NaN             None      Zero Imputation   
 172   XGBoost       NaN             None      Zero Imputation   
 173   XGBoost       NaN             None      Zero Imputation   
 174   XGBoost       NaN             None      Zero Imputation   
 175   XGBoost       NaN             None      Zero Imputation   
 176   XGBoost       NaN             None      Zero Imputation   
 177   XGBoost       NaN             None      Zero Imputation   
 178   XGBoost       NaN             None      Zero Imputation   
 179   XGBoost       NaN             None      Zero Imputation   
 180   XGBoost       NaN             None      Zero Imputation   
 181   XGBoost       NaN             None      Zero Imputation   
 182   XGBoost       NaN             None      Zero Imputation   
 183   XGBoost       NaN             None      Zero Imputation   
 184   XGBoost  0.853793              NaN      Zero Imputation   
 185   XGBoost  0.151951              NaN      Zero Imputation   
 186   XGBoost  0.342593              NaN      Zero Imputation   
 187   XGBoost  0.210526              NaN      Zero Imputation   
 188   XGBoost  0.701327              NaN      Zero Imputation   
 189   XGBoost  0.313423              NaN      Zero Imputation   
 190   XGBoost  0.402654              NaN      Zero Imputation   
 191   XGBoost       NaN  binary:logistic      Zero Imputation   
 192   XGBoost       NaN             None      Zero Imputation   
 193   XGBoost       NaN             None      Zero Imputation   
 194   XGBoost       NaN             None      Zero Imputation   
 195   XGBoost       NaN             None      Zero Imputation   
 196   XGBoost       NaN             None      Zero Imputation   
 197   XGBoost       NaN             None      Zero Imputation   
 198   XGBoost       NaN             None      Zero Imputation   
 199   XGBoost       NaN             None      Zero Imputation   
 200   XGBoost       NaN            False      Zero Imputation   
 201   XGBoost       NaN             None      Zero Imputation   
 202   XGBoost       NaN             None      Zero Imputation   
 203   XGBoost       NaN             None      Zero Imputation   
 204   XGBoost       NaN             None      Zero Imputation   
 205   XGBoost       NaN             None      Zero Imputation   
 206   XGBoost       NaN             None      Zero Imputation   
 207   XGBoost       NaN             None      Zero Imputation   
 208   XGBoost       NaN             None      Zero Imputation   
 209   XGBoost       NaN             None      Zero Imputation   
 210   XGBoost       NaN             None      Zero Imputation   
 211   XGBoost       NaN             None      Zero Imputation   
 212   XGBoost       NaN             None      Zero Imputation   
 213   XGBoost       NaN             None      Zero Imputation   
 214   XGBoost       NaN             None      Zero Imputation   
 215   XGBoost       NaN              NaN      Zero Imputation   
 216   XGBoost       NaN             None      Zero Imputation   
 217   XGBoost       NaN             None      Zero Imputation   
 218   XGBoost       NaN             None      Zero Imputation   
 219   XGBoost       NaN             None      Zero Imputation   
 220   XGBoost       NaN             None      Zero Imputation   
 221   XGBoost       NaN             None      Zero Imputation   
 222   XGBoost       NaN             None      Zero Imputation   
 223   XGBoost       NaN             None      Zero Imputation   
 224   XGBoost       NaN             None      Zero Imputation   
 225   XGBoost       NaN             None      Zero Imputation   
 226   XGBoost       NaN             None      Zero Imputation   
 227   XGBoost       NaN             None      Zero Imputation   
 228   XGBoost       NaN             None      Zero Imputation   
 229   XGBoost       NaN             None      Zero Imputation   
 
     Imbalance Class Technique                            Model Unique Code  
 0                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 1                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 2                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 3                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 4                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 5                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 6                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 7                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 8                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 9                Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 10               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 11               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 12               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 13               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 14               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 15               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 16               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 17               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 18               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 19               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 20               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 21               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 22               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 23               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 24               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 25               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 26               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 27               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 28               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 29               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 30               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 31               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 32               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 33               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 34               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 35               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 36               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 37               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 38               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 39               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 40               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 41               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 42               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 43               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 44               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 45               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_0  
 46               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 47               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 48               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 49               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 50               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 51               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 52               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 53               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 54               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 55               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 56               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 57               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 58               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 59               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 60               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 61               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 62               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 63               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 64               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 65               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 66               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 67               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 68               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 69               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 70               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 71               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 72               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 73               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 74               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 75               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 76               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 77               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 78               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 79               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 80               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 81               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 82               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 83               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 84               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 85               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 86               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 87               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 88               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 89               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 90               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 91               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_1  
 92               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 93               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 94               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 95               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 96               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 97               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 98               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 99               Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 100              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 101              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 102              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 103              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 104              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 105              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 106              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 107              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 108              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 109              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 110              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 111              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 112              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 113              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 114              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 115              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 116              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 117              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 118              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 119              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 120              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 121              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 122              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 123              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 124              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 125              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 126              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 127              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 128              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 129              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 130              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 131              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 132              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 133              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 134              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 135              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 136              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 137              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_2  
 138              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 139              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 140              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 141              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 142              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 143              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 144              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 145              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 146              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 147              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 148              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 149              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 150              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 151              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 152              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 153              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 154              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 155              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 156              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 157              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 158              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 159              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 160              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 161              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 162              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 163              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 164              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 165              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 166              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 167              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 168              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 169              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 170              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 171              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 172              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 173              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 174              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 175              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 176              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 177              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 178              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 179              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 180              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 181              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 182              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 183              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_3  
 184              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 185              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 186              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 187              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 188              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 189              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 190              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 191              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 192              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 193              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 194              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 195              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 196              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 197              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 198              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 199              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 200              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 201              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 202              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 203              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 204              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 205              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 206              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 207              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 208              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 209              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 210              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 211              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 212              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 213              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 214              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 215              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 216              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 217              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 218              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 219              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 220              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 221              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 222              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 223              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 224              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 225              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 226              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 227              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 228              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  
 229              Oversampling  XGBoost_Oversampling_Zero Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.845404              NaN      Mean Imputation   
 1     XGBoost  0.133891              NaN      Mean Imputation   
 2     XGBoost  0.270042              NaN      Mean Imputation   
 3     XGBoost  0.179021              NaN      Mean Imputation   
 4     XGBoost  0.663638              NaN      Mean Imputation   
 5     XGBoost  0.243710              NaN      Mean Imputation   
 6     XGBoost  0.327277              NaN      Mean Imputation   
 7     XGBoost       NaN  binary:logistic      Mean Imputation   
 8     XGBoost       NaN             None      Mean Imputation   
 9     XGBoost       NaN             None      Mean Imputation   
 10    XGBoost       NaN             None      Mean Imputation   
 11    XGBoost       NaN             None      Mean Imputation   
 12    XGBoost       NaN             None      Mean Imputation   
 13    XGBoost       NaN             None      Mean Imputation   
 14    XGBoost       NaN             None      Mean Imputation   
 15    XGBoost       NaN             None      Mean Imputation   
 16    XGBoost       NaN            False      Mean Imputation   
 17    XGBoost       NaN             None      Mean Imputation   
 18    XGBoost       NaN             None      Mean Imputation   
 19    XGBoost       NaN             None      Mean Imputation   
 20    XGBoost       NaN             None      Mean Imputation   
 21    XGBoost       NaN             None      Mean Imputation   
 22    XGBoost       NaN             None      Mean Imputation   
 23    XGBoost       NaN             None      Mean Imputation   
 24    XGBoost       NaN             None      Mean Imputation   
 25    XGBoost       NaN             None      Mean Imputation   
 26    XGBoost       NaN             None      Mean Imputation   
 27    XGBoost       NaN             None      Mean Imputation   
 28    XGBoost       NaN             None      Mean Imputation   
 29    XGBoost       NaN             None      Mean Imputation   
 30    XGBoost       NaN             None      Mean Imputation   
 31    XGBoost       NaN              NaN      Mean Imputation   
 32    XGBoost       NaN             None      Mean Imputation   
 33    XGBoost       NaN             None      Mean Imputation   
 34    XGBoost       NaN             None      Mean Imputation   
 35    XGBoost       NaN             None      Mean Imputation   
 36    XGBoost       NaN             None      Mean Imputation   
 37    XGBoost       NaN             None      Mean Imputation   
 38    XGBoost       NaN             None      Mean Imputation   
 39    XGBoost       NaN             None      Mean Imputation   
 40    XGBoost       NaN             None      Mean Imputation   
 41    XGBoost       NaN             None      Mean Imputation   
 42    XGBoost       NaN             None      Mean Imputation   
 43    XGBoost       NaN             None      Mean Imputation   
 44    XGBoost       NaN             None      Mean Imputation   
 45    XGBoost       NaN             None      Mean Imputation   
 46    XGBoost  0.847511              NaN      Mean Imputation   
 47    XGBoost  0.107750              NaN      Mean Imputation   
 48    XGBoost  0.347561              NaN      Mean Imputation   
 49    XGBoost  0.164502              NaN      Mean Imputation   
 50    XGBoost  0.674761              NaN      Mean Imputation   
 51    XGBoost  0.298336              NaN      Mean Imputation   
 52    XGBoost  0.349521              NaN      Mean Imputation   
 53    XGBoost       NaN  binary:logistic      Mean Imputation   
 54    XGBoost       NaN             None      Mean Imputation   
 55    XGBoost       NaN             None      Mean Imputation   
 56    XGBoost       NaN             None      Mean Imputation   
 57    XGBoost       NaN             None      Mean Imputation   
 58    XGBoost       NaN             None      Mean Imputation   
 59    XGBoost       NaN             None      Mean Imputation   
 60    XGBoost       NaN             None      Mean Imputation   
 61    XGBoost       NaN             None      Mean Imputation   
 62    XGBoost       NaN            False      Mean Imputation   
 63    XGBoost       NaN             None      Mean Imputation   
 64    XGBoost       NaN             None      Mean Imputation   
 65    XGBoost       NaN             None      Mean Imputation   
 66    XGBoost       NaN             None      Mean Imputation   
 67    XGBoost       NaN             None      Mean Imputation   
 68    XGBoost       NaN             None      Mean Imputation   
 69    XGBoost       NaN             None      Mean Imputation   
 70    XGBoost       NaN             None      Mean Imputation   
 71    XGBoost       NaN             None      Mean Imputation   
 72    XGBoost       NaN             None      Mean Imputation   
 73    XGBoost       NaN             None      Mean Imputation   
 74    XGBoost       NaN             None      Mean Imputation   
 75    XGBoost       NaN             None      Mean Imputation   
 76    XGBoost       NaN             None      Mean Imputation   
 77    XGBoost       NaN              NaN      Mean Imputation   
 78    XGBoost       NaN             None      Mean Imputation   
 79    XGBoost       NaN             None      Mean Imputation   
 80    XGBoost       NaN             None      Mean Imputation   
 81    XGBoost       NaN             None      Mean Imputation   
 82    XGBoost       NaN             None      Mean Imputation   
 83    XGBoost       NaN             None      Mean Imputation   
 84    XGBoost       NaN             None      Mean Imputation   
 85    XGBoost       NaN             None      Mean Imputation   
 86    XGBoost       NaN             None      Mean Imputation   
 87    XGBoost       NaN             None      Mean Imputation   
 88    XGBoost       NaN             None      Mean Imputation   
 89    XGBoost       NaN             None      Mean Imputation   
 90    XGBoost       NaN             None      Mean Imputation   
 91    XGBoost       NaN             None      Mean Imputation   
 92    XGBoost  0.843297              NaN      Mean Imputation   
 93    XGBoost  0.127737              NaN      Mean Imputation   
 94    XGBoost  0.374332              NaN      Mean Imputation   
 95    XGBoost  0.190476              NaN      Mean Imputation   
 96    XGBoost  0.683479              NaN      Mean Imputation   
 97    XGBoost  0.260429              NaN      Mean Imputation   
 98    XGBoost  0.366959              NaN      Mean Imputation   
 99    XGBoost       NaN  binary:logistic      Mean Imputation   
 100   XGBoost       NaN             None      Mean Imputation   
 101   XGBoost       NaN             None      Mean Imputation   
 102   XGBoost       NaN             None      Mean Imputation   
 103   XGBoost       NaN             None      Mean Imputation   
 104   XGBoost       NaN             None      Mean Imputation   
 105   XGBoost       NaN             None      Mean Imputation   
 106   XGBoost       NaN             None      Mean Imputation   
 107   XGBoost       NaN             None      Mean Imputation   
 108   XGBoost       NaN            False      Mean Imputation   
 109   XGBoost       NaN             None      Mean Imputation   
 110   XGBoost       NaN             None      Mean Imputation   
 111   XGBoost       NaN             None      Mean Imputation   
 112   XGBoost       NaN             None      Mean Imputation   
 113   XGBoost       NaN             None      Mean Imputation   
 114   XGBoost       NaN             None      Mean Imputation   
 115   XGBoost       NaN             None      Mean Imputation   
 116   XGBoost       NaN             None      Mean Imputation   
 117   XGBoost       NaN             None      Mean Imputation   
 118   XGBoost       NaN             None      Mean Imputation   
 119   XGBoost       NaN             None      Mean Imputation   
 120   XGBoost       NaN             None      Mean Imputation   
 121   XGBoost       NaN             None      Mean Imputation   
 122   XGBoost       NaN             None      Mean Imputation   
 123   XGBoost       NaN              NaN      Mean Imputation   
 124   XGBoost       NaN             None      Mean Imputation   
 125   XGBoost       NaN             None      Mean Imputation   
 126   XGBoost       NaN             None      Mean Imputation   
 127   XGBoost       NaN             None      Mean Imputation   
 128   XGBoost       NaN             None      Mean Imputation   
 129   XGBoost       NaN             None      Mean Imputation   
 130   XGBoost       NaN             None      Mean Imputation   
 131   XGBoost       NaN             None      Mean Imputation   
 132   XGBoost       NaN             None      Mean Imputation   
 133   XGBoost       NaN             None      Mean Imputation   
 134   XGBoost       NaN             None      Mean Imputation   
 135   XGBoost       NaN             None      Mean Imputation   
 136   XGBoost       NaN             None      Mean Imputation   
 137   XGBoost       NaN             None      Mean Imputation   
 138   XGBoost  0.851159              NaN      Mean Imputation   
 139   XGBoost  0.119588              NaN      Mean Imputation   
 140   XGBoost  0.295918              NaN      Mean Imputation   
 141   XGBoost  0.170338              NaN      Mean Imputation   
 142   XGBoost  0.665867              NaN      Mean Imputation   
 143   XGBoost  0.259507              NaN      Mean Imputation   
 144   XGBoost  0.331733              NaN      Mean Imputation   
 145   XGBoost       NaN  binary:logistic      Mean Imputation   
 146   XGBoost       NaN             None      Mean Imputation   
 147   XGBoost       NaN             None      Mean Imputation   
 148   XGBoost       NaN             None      Mean Imputation   
 149   XGBoost       NaN             None      Mean Imputation   
 150   XGBoost       NaN             None      Mean Imputation   
 151   XGBoost       NaN             None      Mean Imputation   
 152   XGBoost       NaN             None      Mean Imputation   
 153   XGBoost       NaN             None      Mean Imputation   
 154   XGBoost       NaN            False      Mean Imputation   
 155   XGBoost       NaN             None      Mean Imputation   
 156   XGBoost       NaN             None      Mean Imputation   
 157   XGBoost       NaN             None      Mean Imputation   
 158   XGBoost       NaN             None      Mean Imputation   
 159   XGBoost       NaN             None      Mean Imputation   
 160   XGBoost       NaN             None      Mean Imputation   
 161   XGBoost       NaN             None      Mean Imputation   
 162   XGBoost       NaN             None      Mean Imputation   
 163   XGBoost       NaN             None      Mean Imputation   
 164   XGBoost       NaN             None      Mean Imputation   
 165   XGBoost       NaN             None      Mean Imputation   
 166   XGBoost       NaN             None      Mean Imputation   
 167   XGBoost       NaN             None      Mean Imputation   
 168   XGBoost       NaN             None      Mean Imputation   
 169   XGBoost       NaN              NaN      Mean Imputation   
 170   XGBoost       NaN             None      Mean Imputation   
 171   XGBoost       NaN             None      Mean Imputation   
 172   XGBoost       NaN             None      Mean Imputation   
 173   XGBoost       NaN             None      Mean Imputation   
 174   XGBoost       NaN             None      Mean Imputation   
 175   XGBoost       NaN             None      Mean Imputation   
 176   XGBoost       NaN             None      Mean Imputation   
 177   XGBoost       NaN             None      Mean Imputation   
 178   XGBoost       NaN             None      Mean Imputation   
 179   XGBoost       NaN             None      Mean Imputation   
 180   XGBoost       NaN             None      Mean Imputation   
 181   XGBoost       NaN             None      Mean Imputation   
 182   XGBoost       NaN             None      Mean Imputation   
 183   XGBoost       NaN             None      Mean Imputation   
 184   XGBoost  0.855638              NaN      Mean Imputation   
 185   XGBoost  0.148305              NaN      Mean Imputation   
 186   XGBoost  0.324074              NaN      Mean Imputation   
 187   XGBoost  0.203488              NaN      Mean Imputation   
 188   XGBoost  0.698515              NaN      Mean Imputation   
 189   XGBoost  0.320458              NaN      Mean Imputation   
 190   XGBoost  0.397030              NaN      Mean Imputation   
 191   XGBoost       NaN  binary:logistic      Mean Imputation   
 192   XGBoost       NaN             None      Mean Imputation   
 193   XGBoost       NaN             None      Mean Imputation   
 194   XGBoost       NaN             None      Mean Imputation   
 195   XGBoost       NaN             None      Mean Imputation   
 196   XGBoost       NaN             None      Mean Imputation   
 197   XGBoost       NaN             None      Mean Imputation   
 198   XGBoost       NaN             None      Mean Imputation   
 199   XGBoost       NaN             None      Mean Imputation   
 200   XGBoost       NaN            False      Mean Imputation   
 201   XGBoost       NaN             None      Mean Imputation   
 202   XGBoost       NaN             None      Mean Imputation   
 203   XGBoost       NaN             None      Mean Imputation   
 204   XGBoost       NaN             None      Mean Imputation   
 205   XGBoost       NaN             None      Mean Imputation   
 206   XGBoost       NaN             None      Mean Imputation   
 207   XGBoost       NaN             None      Mean Imputation   
 208   XGBoost       NaN             None      Mean Imputation   
 209   XGBoost       NaN             None      Mean Imputation   
 210   XGBoost       NaN             None      Mean Imputation   
 211   XGBoost       NaN             None      Mean Imputation   
 212   XGBoost       NaN             None      Mean Imputation   
 213   XGBoost       NaN             None      Mean Imputation   
 214   XGBoost       NaN             None      Mean Imputation   
 215   XGBoost       NaN              NaN      Mean Imputation   
 216   XGBoost       NaN             None      Mean Imputation   
 217   XGBoost       NaN             None      Mean Imputation   
 218   XGBoost       NaN             None      Mean Imputation   
 219   XGBoost       NaN             None      Mean Imputation   
 220   XGBoost       NaN             None      Mean Imputation   
 221   XGBoost       NaN             None      Mean Imputation   
 222   XGBoost       NaN             None      Mean Imputation   
 223   XGBoost       NaN             None      Mean Imputation   
 224   XGBoost       NaN             None      Mean Imputation   
 225   XGBoost       NaN             None      Mean Imputation   
 226   XGBoost       NaN             None      Mean Imputation   
 227   XGBoost       NaN             None      Mean Imputation   
 228   XGBoost       NaN             None      Mean Imputation   
 229   XGBoost       NaN             None      Mean Imputation   
 
     Imbalance Class Technique                            Model Unique Code  
 0                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 1                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 2                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 3                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 4                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 5                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 6                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 7                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 8                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 9                Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 10               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 11               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 12               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 13               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 14               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 15               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 16               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 17               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 18               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 19               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 20               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 21               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 22               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 23               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 24               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 25               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 26               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 27               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 28               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 29               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 30               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 31               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 32               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 33               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 34               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 35               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 36               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 37               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 38               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 39               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 40               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 41               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 42               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 43               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 44               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 45               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_0  
 46               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 47               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 48               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 49               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 50               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 51               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 52               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 53               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 54               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 55               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 56               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 57               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 58               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 59               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 60               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 61               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 62               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 63               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 64               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 65               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 66               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 67               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 68               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 69               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 70               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 71               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 72               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 73               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 74               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 75               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 76               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 77               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 78               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 79               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 80               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 81               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 82               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 83               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 84               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 85               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 86               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 87               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 88               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 89               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 90               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 91               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_1  
 92               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 93               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 94               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 95               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 96               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 97               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 98               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 99               Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 100              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 101              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 102              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 103              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 104              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 105              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 106              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 107              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 108              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 109              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 110              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 111              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 112              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 113              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 114              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 115              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 116              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 117              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 118              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 119              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 120              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 121              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 122              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 123              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 124              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 125              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 126              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 127              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 128              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 129              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 130              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 131              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 132              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 133              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 134              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 135              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 136              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 137              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_2  
 138              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 139              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 140              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 141              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 142              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 143              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 144              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 145              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 146              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 147              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 148              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 149              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 150              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 151              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 152              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 153              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 154              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 155              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 156              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 157              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 158              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 159              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 160              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 161              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 162              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 163              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 164              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 165              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 166              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 167              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 168              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 169              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 170              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 171              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 172              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 173              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 174              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 175              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 176              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 177              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 178              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 179              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 180              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 181              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 182              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 183              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_3  
 184              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 185              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 186              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 187              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 188              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 189              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 190              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 191              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 192              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 193              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 194              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 195              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 196              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 197              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 198              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 199              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 200              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 201              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 202              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 203              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 204              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 205              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 206              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 207              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 208              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 209              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 210              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 211              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 212              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 213              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 214              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 215              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 216              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 217              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 218              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 219              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 220              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 221              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 222              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 223              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 224              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 225              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 226              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 227              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 228              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  
 229              Oversampling  XGBoost_Oversampling_Mean Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.845668              NaN    Median Imputation   
 1     XGBoost  0.129512              NaN    Median Imputation   
 2     XGBoost  0.257384              NaN    Median Imputation   
 3     XGBoost  0.172316              NaN    Median Imputation   
 4     XGBoost  0.661887              NaN    Median Imputation   
 5     XGBoost  0.257361              NaN    Median Imputation   
 6     XGBoost  0.323774              NaN    Median Imputation   
 7     XGBoost       NaN  binary:logistic    Median Imputation   
 8     XGBoost       NaN             None    Median Imputation   
 9     XGBoost       NaN             None    Median Imputation   
 10    XGBoost       NaN             None    Median Imputation   
 11    XGBoost       NaN             None    Median Imputation   
 12    XGBoost       NaN             None    Median Imputation   
 13    XGBoost       NaN             None    Median Imputation   
 14    XGBoost       NaN             None    Median Imputation   
 15    XGBoost       NaN             None    Median Imputation   
 16    XGBoost       NaN            False    Median Imputation   
 17    XGBoost       NaN             None    Median Imputation   
 18    XGBoost       NaN             None    Median Imputation   
 19    XGBoost       NaN             None    Median Imputation   
 20    XGBoost       NaN             None    Median Imputation   
 21    XGBoost       NaN             None    Median Imputation   
 22    XGBoost       NaN             None    Median Imputation   
 23    XGBoost       NaN             None    Median Imputation   
 24    XGBoost       NaN             None    Median Imputation   
 25    XGBoost       NaN             None    Median Imputation   
 26    XGBoost       NaN             None    Median Imputation   
 27    XGBoost       NaN             None    Median Imputation   
 28    XGBoost       NaN             None    Median Imputation   
 29    XGBoost       NaN             None    Median Imputation   
 30    XGBoost       NaN             None    Median Imputation   
 31    XGBoost       NaN              NaN    Median Imputation   
 32    XGBoost       NaN             None    Median Imputation   
 33    XGBoost       NaN             None    Median Imputation   
 34    XGBoost       NaN             None    Median Imputation   
 35    XGBoost       NaN             None    Median Imputation   
 36    XGBoost       NaN             None    Median Imputation   
 37    XGBoost       NaN             None    Median Imputation   
 38    XGBoost       NaN             None    Median Imputation   
 39    XGBoost       NaN             None    Median Imputation   
 40    XGBoost       NaN             None    Median Imputation   
 41    XGBoost       NaN             None    Median Imputation   
 42    XGBoost       NaN             None    Median Imputation   
 43    XGBoost       NaN             None    Median Imputation   
 44    XGBoost       NaN             None    Median Imputation   
 45    XGBoost       NaN             None    Median Imputation   
 46    XGBoost  0.851462              NaN    Median Imputation   
 47    XGBoost  0.100000              NaN    Median Imputation   
 48    XGBoost  0.304878              NaN    Median Imputation   
 49    XGBoost  0.150602              NaN    Median Imputation   
 50    XGBoost  0.672507              NaN    Median Imputation   
 51    XGBoost  0.295607              NaN    Median Imputation   
 52    XGBoost  0.345015              NaN    Median Imputation   
 53    XGBoost       NaN  binary:logistic    Median Imputation   
 54    XGBoost       NaN             None    Median Imputation   
 55    XGBoost       NaN             None    Median Imputation   
 56    XGBoost       NaN             None    Median Imputation   
 57    XGBoost       NaN             None    Median Imputation   
 58    XGBoost       NaN             None    Median Imputation   
 59    XGBoost       NaN             None    Median Imputation   
 60    XGBoost       NaN             None    Median Imputation   
 61    XGBoost       NaN             None    Median Imputation   
 62    XGBoost       NaN            False    Median Imputation   
 63    XGBoost       NaN             None    Median Imputation   
 64    XGBoost       NaN             None    Median Imputation   
 65    XGBoost       NaN             None    Median Imputation   
 66    XGBoost       NaN             None    Median Imputation   
 67    XGBoost       NaN             None    Median Imputation   
 68    XGBoost       NaN             None    Median Imputation   
 69    XGBoost       NaN             None    Median Imputation   
 70    XGBoost       NaN             None    Median Imputation   
 71    XGBoost       NaN             None    Median Imputation   
 72    XGBoost       NaN             None    Median Imputation   
 73    XGBoost       NaN             None    Median Imputation   
 74    XGBoost       NaN             None    Median Imputation   
 75    XGBoost       NaN             None    Median Imputation   
 76    XGBoost       NaN             None    Median Imputation   
 77    XGBoost       NaN              NaN    Median Imputation   
 78    XGBoost       NaN             None    Median Imputation   
 79    XGBoost       NaN             None    Median Imputation   
 80    XGBoost       NaN             None    Median Imputation   
 81    XGBoost       NaN             None    Median Imputation   
 82    XGBoost       NaN             None    Median Imputation   
 83    XGBoost       NaN             None    Median Imputation   
 84    XGBoost       NaN             None    Median Imputation   
 85    XGBoost       NaN             None    Median Imputation   
 86    XGBoost       NaN             None    Median Imputation   
 87    XGBoost       NaN             None    Median Imputation   
 88    XGBoost       NaN             None    Median Imputation   
 89    XGBoost       NaN             None    Median Imputation   
 90    XGBoost       NaN             None    Median Imputation   
 91    XGBoost       NaN             None    Median Imputation   
 92    XGBoost  0.855676              NaN    Median Imputation   
 93    XGBoost  0.132383              NaN    Median Imputation   
 94    XGBoost  0.347594              NaN    Median Imputation   
 95    XGBoost  0.191740              NaN    Median Imputation   
 96    XGBoost  0.690471              NaN    Median Imputation   
 97    XGBoost  0.299796              NaN    Median Imputation   
 98    XGBoost  0.380943              NaN    Median Imputation   
 99    XGBoost       NaN  binary:logistic    Median Imputation   
 100   XGBoost       NaN             None    Median Imputation   
 101   XGBoost       NaN             None    Median Imputation   
 102   XGBoost       NaN             None    Median Imputation   
 103   XGBoost       NaN             None    Median Imputation   
 104   XGBoost       NaN             None    Median Imputation   
 105   XGBoost       NaN             None    Median Imputation   
 106   XGBoost       NaN             None    Median Imputation   
 107   XGBoost       NaN             None    Median Imputation   
 108   XGBoost       NaN            False    Median Imputation   
 109   XGBoost       NaN             None    Median Imputation   
 110   XGBoost       NaN             None    Median Imputation   
 111   XGBoost       NaN             None    Median Imputation   
 112   XGBoost       NaN             None    Median Imputation   
 113   XGBoost       NaN             None    Median Imputation   
 114   XGBoost       NaN             None    Median Imputation   
 115   XGBoost       NaN             None    Median Imputation   
 116   XGBoost       NaN             None    Median Imputation   
 117   XGBoost       NaN             None    Median Imputation   
 118   XGBoost       NaN             None    Median Imputation   
 119   XGBoost       NaN             None    Median Imputation   
 120   XGBoost       NaN             None    Median Imputation   
 121   XGBoost       NaN             None    Median Imputation   
 122   XGBoost       NaN             None    Median Imputation   
 123   XGBoost       NaN              NaN    Median Imputation   
 124   XGBoost       NaN             None    Median Imputation   
 125   XGBoost       NaN             None    Median Imputation   
 126   XGBoost       NaN             None    Median Imputation   
 127   XGBoost       NaN             None    Median Imputation   
 128   XGBoost       NaN             None    Median Imputation   
 129   XGBoost       NaN             None    Median Imputation   
 130   XGBoost       NaN             None    Median Imputation   
 131   XGBoost       NaN             None    Median Imputation   
 132   XGBoost       NaN             None    Median Imputation   
 133   XGBoost       NaN             None    Median Imputation   
 134   XGBoost       NaN             None    Median Imputation   
 135   XGBoost       NaN             None    Median Imputation   
 136   XGBoost       NaN             None    Median Imputation   
 137   XGBoost       NaN             None    Median Imputation   
 138   XGBoost  0.852740              NaN    Median Imputation   
 139   XGBoost  0.125773              NaN    Median Imputation   
 140   XGBoost  0.311224              NaN    Median Imputation   
 141   XGBoost  0.179148              NaN    Median Imputation   
 142   XGBoost  0.657651              NaN    Median Imputation   
 143   XGBoost  0.238838              NaN    Median Imputation   
 144   XGBoost  0.315302              NaN    Median Imputation   
 145   XGBoost       NaN  binary:logistic    Median Imputation   
 146   XGBoost       NaN             None    Median Imputation   
 147   XGBoost       NaN             None    Median Imputation   
 148   XGBoost       NaN             None    Median Imputation   
 149   XGBoost       NaN             None    Median Imputation   
 150   XGBoost       NaN             None    Median Imputation   
 151   XGBoost       NaN             None    Median Imputation   
 152   XGBoost       NaN             None    Median Imputation   
 153   XGBoost       NaN             None    Median Imputation   
 154   XGBoost       NaN            False    Median Imputation   
 155   XGBoost       NaN             None    Median Imputation   
 156   XGBoost       NaN             None    Median Imputation   
 157   XGBoost       NaN             None    Median Imputation   
 158   XGBoost       NaN             None    Median Imputation   
 159   XGBoost       NaN             None    Median Imputation   
 160   XGBoost       NaN             None    Median Imputation   
 161   XGBoost       NaN             None    Median Imputation   
 162   XGBoost       NaN             None    Median Imputation   
 163   XGBoost       NaN             None    Median Imputation   
 164   XGBoost       NaN             None    Median Imputation   
 165   XGBoost       NaN             None    Median Imputation   
 166   XGBoost       NaN             None    Median Imputation   
 167   XGBoost       NaN             None    Median Imputation   
 168   XGBoost       NaN             None    Median Imputation   
 169   XGBoost       NaN              NaN    Median Imputation   
 170   XGBoost       NaN             None    Median Imputation   
 171   XGBoost       NaN             None    Median Imputation   
 172   XGBoost       NaN             None    Median Imputation   
 173   XGBoost       NaN             None    Median Imputation   
 174   XGBoost       NaN             None    Median Imputation   
 175   XGBoost       NaN             None    Median Imputation   
 176   XGBoost       NaN             None    Median Imputation   
 177   XGBoost       NaN             None    Median Imputation   
 178   XGBoost       NaN             None    Median Imputation   
 179   XGBoost       NaN             None    Median Imputation   
 180   XGBoost       NaN             None    Median Imputation   
 181   XGBoost       NaN             None    Median Imputation   
 182   XGBoost       NaN             None    Median Imputation   
 183   XGBoost       NaN             None    Median Imputation   
 184   XGBoost  0.862487              NaN    Median Imputation   
 185   XGBoost  0.147465              NaN    Median Imputation   
 186   XGBoost  0.296296              NaN    Median Imputation   
 187   XGBoost  0.196923              NaN    Median Imputation   
 188   XGBoost  0.688897              NaN    Median Imputation   
 189   XGBoost  0.288827              NaN    Median Imputation   
 190   XGBoost  0.377795              NaN    Median Imputation   
 191   XGBoost       NaN  binary:logistic    Median Imputation   
 192   XGBoost       NaN             None    Median Imputation   
 193   XGBoost       NaN             None    Median Imputation   
 194   XGBoost       NaN             None    Median Imputation   
 195   XGBoost       NaN             None    Median Imputation   
 196   XGBoost       NaN             None    Median Imputation   
 197   XGBoost       NaN             None    Median Imputation   
 198   XGBoost       NaN             None    Median Imputation   
 199   XGBoost       NaN             None    Median Imputation   
 200   XGBoost       NaN            False    Median Imputation   
 201   XGBoost       NaN             None    Median Imputation   
 202   XGBoost       NaN             None    Median Imputation   
 203   XGBoost       NaN             None    Median Imputation   
 204   XGBoost       NaN             None    Median Imputation   
 205   XGBoost       NaN             None    Median Imputation   
 206   XGBoost       NaN             None    Median Imputation   
 207   XGBoost       NaN             None    Median Imputation   
 208   XGBoost       NaN             None    Median Imputation   
 209   XGBoost       NaN             None    Median Imputation   
 210   XGBoost       NaN             None    Median Imputation   
 211   XGBoost       NaN             None    Median Imputation   
 212   XGBoost       NaN             None    Median Imputation   
 213   XGBoost       NaN             None    Median Imputation   
 214   XGBoost       NaN             None    Median Imputation   
 215   XGBoost       NaN              NaN    Median Imputation   
 216   XGBoost       NaN             None    Median Imputation   
 217   XGBoost       NaN             None    Median Imputation   
 218   XGBoost       NaN             None    Median Imputation   
 219   XGBoost       NaN             None    Median Imputation   
 220   XGBoost       NaN             None    Median Imputation   
 221   XGBoost       NaN             None    Median Imputation   
 222   XGBoost       NaN             None    Median Imputation   
 223   XGBoost       NaN             None    Median Imputation   
 224   XGBoost       NaN             None    Median Imputation   
 225   XGBoost       NaN             None    Median Imputation   
 226   XGBoost       NaN             None    Median Imputation   
 227   XGBoost       NaN             None    Median Imputation   
 228   XGBoost       NaN             None    Median Imputation   
 229   XGBoost       NaN             None    Median Imputation   
 
     Imbalance Class Technique                              Model Unique Code  
 0                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 1                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 2                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 3                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 4                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 5                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 6                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 7                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 8                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 9                Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 10               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 11               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 12               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 13               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 14               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 15               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 16               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 17               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 18               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 19               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 20               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 21               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 22               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 23               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 24               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 25               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 26               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 27               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 28               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 29               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 30               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 31               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 32               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 33               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 34               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 35               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 36               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 37               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 38               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 39               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 40               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 41               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 42               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 43               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 44               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 45               Oversampling  XGBoost_Oversampling_Median Imputation_fold_0  
 46               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 47               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 48               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 49               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 50               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 51               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 52               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 53               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 54               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 55               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 56               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 57               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 58               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 59               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 60               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 61               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 62               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 63               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 64               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 65               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 66               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 67               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 68               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 69               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 70               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 71               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 72               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 73               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 74               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 75               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 76               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 77               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 78               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 79               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 80               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 81               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 82               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 83               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 84               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 85               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 86               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 87               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 88               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 89               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 90               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 91               Oversampling  XGBoost_Oversampling_Median Imputation_fold_1  
 92               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 93               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 94               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 95               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 96               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 97               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 98               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 99               Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 100              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 101              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 102              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 103              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 104              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 105              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 106              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 107              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 108              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 109              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 110              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 111              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 112              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 113              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 114              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 115              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 116              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 117              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 118              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 119              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 120              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 121              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 122              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 123              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 124              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 125              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 126              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 127              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 128              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 129              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 130              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 131              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 132              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 133              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 134              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 135              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 136              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 137              Oversampling  XGBoost_Oversampling_Median Imputation_fold_2  
 138              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 139              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 140              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 141              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 142              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 143              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 144              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 145              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 146              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 147              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 148              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 149              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 150              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 151              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 152              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 153              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 154              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 155              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 156              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 157              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 158              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 159              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 160              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 161              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 162              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 163              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 164              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 165              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 166              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 167              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 168              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 169              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 170              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 171              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 172              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 173              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 174              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 175              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 176              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 177              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 178              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 179              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 180              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 181              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 182              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 183              Oversampling  XGBoost_Oversampling_Median Imputation_fold_3  
 184              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 185              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 186              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 187              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 188              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 189              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 190              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 191              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 192              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 193              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 194              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 195              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 196              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 197              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 198              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 199              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 200              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 201              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 202              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 203              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 204              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 205              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 206              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 207              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 208              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 209              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 210              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 211              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 212              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 213              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 214              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 215              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 216              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 217              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 218              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 219              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 220              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 221              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 222              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 223              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 224              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 225              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 226              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 227              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 228              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  
 229              Oversampling  XGBoost_Oversampling_Median Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.646826              NaN      Zero Imputation   
 1     XGBoost  0.106277              NaN      Zero Imputation   
 2     XGBoost  0.628692              NaN      Zero Imputation   
 3     XGBoost  0.181818              NaN      Zero Imputation   
 4     XGBoost  0.686344              NaN      Zero Imputation   
 5     XGBoost  0.311652              NaN      Zero Imputation   
 6     XGBoost  0.372689              NaN      Zero Imputation   
 7     XGBoost       NaN  binary:logistic      Zero Imputation   
 8     XGBoost       NaN             None      Zero Imputation   
 9     XGBoost       NaN             None      Zero Imputation   
 10    XGBoost       NaN             None      Zero Imputation   
 11    XGBoost       NaN             None      Zero Imputation   
 12    XGBoost       NaN             None      Zero Imputation   
 13    XGBoost       NaN             None      Zero Imputation   
 14    XGBoost       NaN             None      Zero Imputation   
 15    XGBoost       NaN             None      Zero Imputation   
 16    XGBoost       NaN            False      Zero Imputation   
 17    XGBoost       NaN             None      Zero Imputation   
 18    XGBoost       NaN             None      Zero Imputation   
 19    XGBoost       NaN             None      Zero Imputation   
 20    XGBoost       NaN             None      Zero Imputation   
 21    XGBoost       NaN             None      Zero Imputation   
 22    XGBoost       NaN             None      Zero Imputation   
 23    XGBoost       NaN             None      Zero Imputation   
 24    XGBoost       NaN             None      Zero Imputation   
 25    XGBoost       NaN             None      Zero Imputation   
 26    XGBoost       NaN             None      Zero Imputation   
 27    XGBoost       NaN             None      Zero Imputation   
 28    XGBoost       NaN             None      Zero Imputation   
 29    XGBoost       NaN             None      Zero Imputation   
 30    XGBoost       NaN             None      Zero Imputation   
 31    XGBoost       NaN              NaN      Zero Imputation   
 32    XGBoost       NaN             None      Zero Imputation   
 33    XGBoost       NaN             None      Zero Imputation   
 34    XGBoost       NaN             None      Zero Imputation   
 35    XGBoost       NaN             None      Zero Imputation   
 36    XGBoost       NaN             None      Zero Imputation   
 37    XGBoost       NaN             None      Zero Imputation   
 38    XGBoost       NaN             None      Zero Imputation   
 39    XGBoost       NaN             None      Zero Imputation   
 40    XGBoost       NaN             None      Zero Imputation   
 41    XGBoost       NaN             None      Zero Imputation   
 42    XGBoost       NaN             None      Zero Imputation   
 43    XGBoost       NaN             None      Zero Imputation   
 44    XGBoost       NaN             None      Zero Imputation   
 45    XGBoost       NaN             None      Zero Imputation   
 46    XGBoost  0.614169              NaN      Zero Imputation   
 47    XGBoost  0.068348              NaN      Zero Imputation   
 48    XGBoost  0.628049              NaN      Zero Imputation   
 49    XGBoost  0.123279              NaN      Zero Imputation   
 50    XGBoost  0.670874              NaN      Zero Imputation   
 51    XGBoost  0.273534              NaN      Zero Imputation   
 52    XGBoost  0.341749              NaN      Zero Imputation   
 53    XGBoost       NaN  binary:logistic      Zero Imputation   
 54    XGBoost       NaN             None      Zero Imputation   
 55    XGBoost       NaN             None      Zero Imputation   
 56    XGBoost       NaN             None      Zero Imputation   
 57    XGBoost       NaN             None      Zero Imputation   
 58    XGBoost       NaN             None      Zero Imputation   
 59    XGBoost       NaN             None      Zero Imputation   
 60    XGBoost       NaN             None      Zero Imputation   
 61    XGBoost       NaN             None      Zero Imputation   
 62    XGBoost       NaN            False      Zero Imputation   
 63    XGBoost       NaN             None      Zero Imputation   
 64    XGBoost       NaN             None      Zero Imputation   
 65    XGBoost       NaN             None      Zero Imputation   
 66    XGBoost       NaN             None      Zero Imputation   
 67    XGBoost       NaN             None      Zero Imputation   
 68    XGBoost       NaN             None      Zero Imputation   
 69    XGBoost       NaN             None      Zero Imputation   
 70    XGBoost       NaN             None      Zero Imputation   
 71    XGBoost       NaN             None      Zero Imputation   
 72    XGBoost       NaN             None      Zero Imputation   
 73    XGBoost       NaN             None      Zero Imputation   
 74    XGBoost       NaN             None      Zero Imputation   
 75    XGBoost       NaN             None      Zero Imputation   
 76    XGBoost       NaN             None      Zero Imputation   
 77    XGBoost       NaN              NaN      Zero Imputation   
 78    XGBoost       NaN             None      Zero Imputation   
 79    XGBoost       NaN             None      Zero Imputation   
 80    XGBoost       NaN             None      Zero Imputation   
 81    XGBoost       NaN             None      Zero Imputation   
 82    XGBoost       NaN             None      Zero Imputation   
 83    XGBoost       NaN             None      Zero Imputation   
 84    XGBoost       NaN             None      Zero Imputation   
 85    XGBoost       NaN             None      Zero Imputation   
 86    XGBoost       NaN             None      Zero Imputation   
 87    XGBoost       NaN             None      Zero Imputation   
 88    XGBoost       NaN             None      Zero Imputation   
 89    XGBoost       NaN             None      Zero Imputation   
 90    XGBoost       NaN             None      Zero Imputation   
 91    XGBoost       NaN             None      Zero Imputation   
 92    XGBoost  0.619173              NaN      Zero Imputation   
 93    XGBoost  0.082283              NaN      Zero Imputation   
 94    XGBoost  0.663102              NaN      Zero Imputation   
 95    XGBoost  0.146399              NaN      Zero Imputation   
 96    XGBoost  0.673209              NaN      Zero Imputation   
 97    XGBoost  0.299799              NaN      Zero Imputation   
 98    XGBoost  0.346417              NaN      Zero Imputation   
 99    XGBoost       NaN  binary:logistic      Zero Imputation   
 100   XGBoost       NaN             None      Zero Imputation   
 101   XGBoost       NaN             None      Zero Imputation   
 102   XGBoost       NaN             None      Zero Imputation   
 103   XGBoost       NaN             None      Zero Imputation   
 104   XGBoost       NaN             None      Zero Imputation   
 105   XGBoost       NaN             None      Zero Imputation   
 106   XGBoost       NaN             None      Zero Imputation   
 107   XGBoost       NaN             None      Zero Imputation   
 108   XGBoost       NaN            False      Zero Imputation   
 109   XGBoost       NaN             None      Zero Imputation   
 110   XGBoost       NaN             None      Zero Imputation   
 111   XGBoost       NaN             None      Zero Imputation   
 112   XGBoost       NaN             None      Zero Imputation   
 113   XGBoost       NaN             None      Zero Imputation   
 114   XGBoost       NaN             None      Zero Imputation   
 115   XGBoost       NaN             None      Zero Imputation   
 116   XGBoost       NaN             None      Zero Imputation   
 117   XGBoost       NaN             None      Zero Imputation   
 118   XGBoost       NaN             None      Zero Imputation   
 119   XGBoost       NaN             None      Zero Imputation   
 120   XGBoost       NaN             None      Zero Imputation   
 121   XGBoost       NaN             None      Zero Imputation   
 122   XGBoost       NaN             None      Zero Imputation   
 123   XGBoost       NaN              NaN      Zero Imputation   
 124   XGBoost       NaN             None      Zero Imputation   
 125   XGBoost       NaN             None      Zero Imputation   
 126   XGBoost       NaN             None      Zero Imputation   
 127   XGBoost       NaN             None      Zero Imputation   
 128   XGBoost       NaN             None      Zero Imputation   
 129   XGBoost       NaN             None      Zero Imputation   
 130   XGBoost       NaN             None      Zero Imputation   
 131   XGBoost       NaN             None      Zero Imputation   
 132   XGBoost       NaN             None      Zero Imputation   
 133   XGBoost       NaN             None      Zero Imputation   
 134   XGBoost       NaN             None      Zero Imputation   
 135   XGBoost       NaN             None      Zero Imputation   
 136   XGBoost       NaN             None      Zero Imputation   
 137   XGBoost       NaN             None      Zero Imputation   
 138   XGBoost  0.629347              NaN      Zero Imputation   
 139   XGBoost  0.084420              NaN      Zero Imputation   
 140   XGBoost  0.627551              NaN      Zero Imputation   
 141   XGBoost  0.148820              NaN      Zero Imputation   
 142   XGBoost  0.648162              NaN      Zero Imputation   
 143   XGBoost  0.269495              NaN      Zero Imputation   
 144   XGBoost  0.296324              NaN      Zero Imputation   
 145   XGBoost       NaN  binary:logistic      Zero Imputation   
 146   XGBoost       NaN             None      Zero Imputation   
 147   XGBoost       NaN             None      Zero Imputation   
 148   XGBoost       NaN             None      Zero Imputation   
 149   XGBoost       NaN             None      Zero Imputation   
 150   XGBoost       NaN             None      Zero Imputation   
 151   XGBoost       NaN             None      Zero Imputation   
 152   XGBoost       NaN             None      Zero Imputation   
 153   XGBoost       NaN             None      Zero Imputation   
 154   XGBoost       NaN            False      Zero Imputation   
 155   XGBoost       NaN             None      Zero Imputation   
 156   XGBoost       NaN             None      Zero Imputation   
 157   XGBoost       NaN             None      Zero Imputation   
 158   XGBoost       NaN             None      Zero Imputation   
 159   XGBoost       NaN             None      Zero Imputation   
 160   XGBoost       NaN             None      Zero Imputation   
 161   XGBoost       NaN             None      Zero Imputation   
 162   XGBoost       NaN             None      Zero Imputation   
 163   XGBoost       NaN             None      Zero Imputation   
 164   XGBoost       NaN             None      Zero Imputation   
 165   XGBoost       NaN             None      Zero Imputation   
 166   XGBoost       NaN             None      Zero Imputation   
 167   XGBoost       NaN             None      Zero Imputation   
 168   XGBoost       NaN             None      Zero Imputation   
 169   XGBoost       NaN              NaN      Zero Imputation   
 170   XGBoost       NaN             None      Zero Imputation   
 171   XGBoost       NaN             None      Zero Imputation   
 172   XGBoost       NaN             None      Zero Imputation   
 173   XGBoost       NaN             None      Zero Imputation   
 174   XGBoost       NaN             None      Zero Imputation   
 175   XGBoost       NaN             None      Zero Imputation   
 176   XGBoost       NaN             None      Zero Imputation   
 177   XGBoost       NaN             None      Zero Imputation   
 178   XGBoost       NaN             None      Zero Imputation   
 179   XGBoost       NaN             None      Zero Imputation   
 180   XGBoost       NaN             None      Zero Imputation   
 181   XGBoost       NaN             None      Zero Imputation   
 182   XGBoost       NaN             None      Zero Imputation   
 183   XGBoost       NaN             None      Zero Imputation   
 184   XGBoost  0.624868              NaN      Zero Imputation   
 185   XGBoost  0.096257              NaN      Zero Imputation   
 186   XGBoost  0.666667              NaN      Zero Imputation   
 187   XGBoost  0.168224              NaN      Zero Imputation   
 188   XGBoost  0.693474              NaN      Zero Imputation   
 189   XGBoost  0.306590              NaN      Zero Imputation   
 190   XGBoost  0.386948              NaN      Zero Imputation   
 191   XGBoost       NaN  binary:logistic      Zero Imputation   
 192   XGBoost       NaN             None      Zero Imputation   
 193   XGBoost       NaN             None      Zero Imputation   
 194   XGBoost       NaN             None      Zero Imputation   
 195   XGBoost       NaN             None      Zero Imputation   
 196   XGBoost       NaN             None      Zero Imputation   
 197   XGBoost       NaN             None      Zero Imputation   
 198   XGBoost       NaN             None      Zero Imputation   
 199   XGBoost       NaN             None      Zero Imputation   
 200   XGBoost       NaN            False      Zero Imputation   
 201   XGBoost       NaN             None      Zero Imputation   
 202   XGBoost       NaN             None      Zero Imputation   
 203   XGBoost       NaN             None      Zero Imputation   
 204   XGBoost       NaN             None      Zero Imputation   
 205   XGBoost       NaN             None      Zero Imputation   
 206   XGBoost       NaN             None      Zero Imputation   
 207   XGBoost       NaN             None      Zero Imputation   
 208   XGBoost       NaN             None      Zero Imputation   
 209   XGBoost       NaN             None      Zero Imputation   
 210   XGBoost       NaN             None      Zero Imputation   
 211   XGBoost       NaN             None      Zero Imputation   
 212   XGBoost       NaN             None      Zero Imputation   
 213   XGBoost       NaN             None      Zero Imputation   
 214   XGBoost       NaN             None      Zero Imputation   
 215   XGBoost       NaN              NaN      Zero Imputation   
 216   XGBoost       NaN             None      Zero Imputation   
 217   XGBoost       NaN             None      Zero Imputation   
 218   XGBoost       NaN             None      Zero Imputation   
 219   XGBoost       NaN             None      Zero Imputation   
 220   XGBoost       NaN             None      Zero Imputation   
 221   XGBoost       NaN             None      Zero Imputation   
 222   XGBoost       NaN             None      Zero Imputation   
 223   XGBoost       NaN             None      Zero Imputation   
 224   XGBoost       NaN             None      Zero Imputation   
 225   XGBoost       NaN             None      Zero Imputation   
 226   XGBoost       NaN             None      Zero Imputation   
 227   XGBoost       NaN             None      Zero Imputation   
 228   XGBoost       NaN             None      Zero Imputation   
 229   XGBoost       NaN             None      Zero Imputation   
 
     Imbalance Class Technique                             Model Unique Code  
 0               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 1               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 2               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 3               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 4               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 5               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 6               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 7               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 8               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 9               Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 10              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 11              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 12              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 13              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 14              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 15              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 16              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 17              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 18              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 19              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 20              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 21              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 22              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 23              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 24              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 25              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 26              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 27              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 28              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 29              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 30              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 31              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 32              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 33              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 34              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 35              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 36              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 37              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 38              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 39              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 40              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 41              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 42              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 43              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 44              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 45              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_0  
 46              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 47              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 48              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 49              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 50              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 51              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 52              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 53              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 54              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 55              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 56              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 57              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 58              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 59              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 60              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 61              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 62              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 63              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 64              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 65              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 66              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 67              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 68              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 69              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 70              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 71              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 72              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 73              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 74              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 75              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 76              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 77              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 78              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 79              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 80              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 81              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 82              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 83              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 84              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 85              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 86              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 87              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 88              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 89              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 90              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 91              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_1  
 92              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 93              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 94              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 95              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 96              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 97              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 98              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 99              Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 100             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 101             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 102             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 103             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 104             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 105             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 106             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 107             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 108             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 109             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 110             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 111             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 112             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 113             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 114             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 115             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 116             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 117             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 118             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 119             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 120             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 121             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 122             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 123             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 124             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 125             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 126             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 127             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 128             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 129             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 130             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 131             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 132             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 133             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 134             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 135             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 136             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 137             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_2  
 138             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 139             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 140             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 141             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 142             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 143             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 144             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 145             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 146             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 147             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 148             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 149             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 150             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 151             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 152             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 153             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 154             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 155             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 156             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 157             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 158             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 159             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 160             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 161             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 162             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 163             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 164             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 165             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 166             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 167             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 168             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 169             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 170             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 171             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 172             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 173             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 174             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 175             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 176             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 177             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 178             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 179             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 180             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 181             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 182             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 183             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_3  
 184             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 185             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 186             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 187             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 188             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 189             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 190             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 191             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 192             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 193             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 194             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 195             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 196             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 197             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 198             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 199             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 200             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 201             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 202             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 203             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 204             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 205             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 206             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 207             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 208             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 209             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 210             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 211             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 212             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 213             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 214             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 215             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 216             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 217             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 218             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 219             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 220             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 221             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 222             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 223             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 224             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 225             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 226             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 227             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 228             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  
 229             Undersampling  XGBoost_Undersampling_Zero Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.634975              NaN      Mean Imputation   
 1     XGBoost  0.105697              NaN      Mean Imputation   
 2     XGBoost  0.649789              NaN      Mean Imputation   
 3     XGBoost  0.181818              NaN      Mean Imputation   
 4     XGBoost  0.666268              NaN      Mean Imputation   
 5     XGBoost  0.292480              NaN      Mean Imputation   
 6     XGBoost  0.332537              NaN      Mean Imputation   
 7     XGBoost       NaN  binary:logistic      Mean Imputation   
 8     XGBoost       NaN             None      Mean Imputation   
 9     XGBoost       NaN             None      Mean Imputation   
 10    XGBoost       NaN             None      Mean Imputation   
 11    XGBoost       NaN             None      Mean Imputation   
 12    XGBoost       NaN             None      Mean Imputation   
 13    XGBoost       NaN             None      Mean Imputation   
 14    XGBoost       NaN             None      Mean Imputation   
 15    XGBoost       NaN             None      Mean Imputation   
 16    XGBoost       NaN            False      Mean Imputation   
 17    XGBoost       NaN             None      Mean Imputation   
 18    XGBoost       NaN             None      Mean Imputation   
 19    XGBoost       NaN             None      Mean Imputation   
 20    XGBoost       NaN             None      Mean Imputation   
 21    XGBoost       NaN             None      Mean Imputation   
 22    XGBoost       NaN             None      Mean Imputation   
 23    XGBoost       NaN             None      Mean Imputation   
 24    XGBoost       NaN             None      Mean Imputation   
 25    XGBoost       NaN             None      Mean Imputation   
 26    XGBoost       NaN             None      Mean Imputation   
 27    XGBoost       NaN             None      Mean Imputation   
 28    XGBoost       NaN             None      Mean Imputation   
 29    XGBoost       NaN             None      Mean Imputation   
 30    XGBoost       NaN             None      Mean Imputation   
 31    XGBoost       NaN              NaN      Mean Imputation   
 32    XGBoost       NaN             None      Mean Imputation   
 33    XGBoost       NaN             None      Mean Imputation   
 34    XGBoost       NaN             None      Mean Imputation   
 35    XGBoost       NaN             None      Mean Imputation   
 36    XGBoost       NaN             None      Mean Imputation   
 37    XGBoost       NaN             None      Mean Imputation   
 38    XGBoost       NaN             None      Mean Imputation   
 39    XGBoost       NaN             None      Mean Imputation   
 40    XGBoost       NaN             None      Mean Imputation   
 41    XGBoost       NaN             None      Mean Imputation   
 42    XGBoost       NaN             None      Mean Imputation   
 43    XGBoost       NaN             None      Mean Imputation   
 44    XGBoost       NaN             None      Mean Imputation   
 45    XGBoost       NaN             None      Mean Imputation   
 46    XGBoost  0.631815              NaN      Mean Imputation   
 47    XGBoost  0.077397              NaN      Mean Imputation   
 48    XGBoost  0.689024              NaN      Mean Imputation   
 49    XGBoost  0.139163              NaN      Mean Imputation   
 50    XGBoost  0.709903              NaN      Mean Imputation   
 51    XGBoost  0.345441              NaN      Mean Imputation   
 52    XGBoost  0.419807              NaN      Mean Imputation   
 53    XGBoost       NaN  binary:logistic      Mean Imputation   
 54    XGBoost       NaN             None      Mean Imputation   
 55    XGBoost       NaN             None      Mean Imputation   
 56    XGBoost       NaN             None      Mean Imputation   
 57    XGBoost       NaN             None      Mean Imputation   
 58    XGBoost       NaN             None      Mean Imputation   
 59    XGBoost       NaN             None      Mean Imputation   
 60    XGBoost       NaN             None      Mean Imputation   
 61    XGBoost       NaN             None      Mean Imputation   
 62    XGBoost       NaN            False      Mean Imputation   
 63    XGBoost       NaN             None      Mean Imputation   
 64    XGBoost       NaN             None      Mean Imputation   
 65    XGBoost       NaN             None      Mean Imputation   
 66    XGBoost       NaN             None      Mean Imputation   
 67    XGBoost       NaN             None      Mean Imputation   
 68    XGBoost       NaN             None      Mean Imputation   
 69    XGBoost       NaN             None      Mean Imputation   
 70    XGBoost       NaN             None      Mean Imputation   
 71    XGBoost       NaN             None      Mean Imputation   
 72    XGBoost       NaN             None      Mean Imputation   
 73    XGBoost       NaN             None      Mean Imputation   
 74    XGBoost       NaN             None      Mean Imputation   
 75    XGBoost       NaN             None      Mean Imputation   
 76    XGBoost       NaN             None      Mean Imputation   
 77    XGBoost       NaN              NaN      Mean Imputation   
 78    XGBoost       NaN             None      Mean Imputation   
 79    XGBoost       NaN             None      Mean Imputation   
 80    XGBoost       NaN             None      Mean Imputation   
 81    XGBoost       NaN             None      Mean Imputation   
 82    XGBoost       NaN             None      Mean Imputation   
 83    XGBoost       NaN             None      Mean Imputation   
 84    XGBoost       NaN             None      Mean Imputation   
 85    XGBoost       NaN             None      Mean Imputation   
 86    XGBoost       NaN             None      Mean Imputation   
 87    XGBoost       NaN             None      Mean Imputation   
 88    XGBoost       NaN             None      Mean Imputation   
 89    XGBoost       NaN             None      Mean Imputation   
 90    XGBoost       NaN             None      Mean Imputation   
 91    XGBoost       NaN             None      Mean Imputation   
 92    XGBoost  0.628391              NaN      Mean Imputation   
 93    XGBoost  0.080822              NaN      Mean Imputation   
 94    XGBoost  0.631016              NaN      Mean Imputation   
 95    XGBoost  0.143291              NaN      Mean Imputation   
 96    XGBoost  0.669068              NaN      Mean Imputation   
 97    XGBoost  0.279817              NaN      Mean Imputation   
 98    XGBoost  0.338135              NaN      Mean Imputation   
 99    XGBoost       NaN  binary:logistic      Mean Imputation   
 100   XGBoost       NaN             None      Mean Imputation   
 101   XGBoost       NaN             None      Mean Imputation   
 102   XGBoost       NaN             None      Mean Imputation   
 103   XGBoost       NaN             None      Mean Imputation   
 104   XGBoost       NaN             None      Mean Imputation   
 105   XGBoost       NaN             None      Mean Imputation   
 106   XGBoost       NaN             None      Mean Imputation   
 107   XGBoost       NaN             None      Mean Imputation   
 108   XGBoost       NaN            False      Mean Imputation   
 109   XGBoost       NaN             None      Mean Imputation   
 110   XGBoost       NaN             None      Mean Imputation   
 111   XGBoost       NaN             None      Mean Imputation   
 112   XGBoost       NaN             None      Mean Imputation   
 113   XGBoost       NaN             None      Mean Imputation   
 114   XGBoost       NaN             None      Mean Imputation   
 115   XGBoost       NaN             None      Mean Imputation   
 116   XGBoost       NaN             None      Mean Imputation   
 117   XGBoost       NaN             None      Mean Imputation   
 118   XGBoost       NaN             None      Mean Imputation   
 119   XGBoost       NaN             None      Mean Imputation   
 120   XGBoost       NaN             None      Mean Imputation   
 121   XGBoost       NaN             None      Mean Imputation   
 122   XGBoost       NaN             None      Mean Imputation   
 123   XGBoost       NaN              NaN      Mean Imputation   
 124   XGBoost       NaN             None      Mean Imputation   
 125   XGBoost       NaN             None      Mean Imputation   
 126   XGBoost       NaN             None      Mean Imputation   
 127   XGBoost       NaN             None      Mean Imputation   
 128   XGBoost       NaN             None      Mean Imputation   
 129   XGBoost       NaN             None      Mean Imputation   
 130   XGBoost       NaN             None      Mean Imputation   
 131   XGBoost       NaN             None      Mean Imputation   
 132   XGBoost       NaN             None      Mean Imputation   
 133   XGBoost       NaN             None      Mean Imputation   
 134   XGBoost       NaN             None      Mean Imputation   
 135   XGBoost       NaN             None      Mean Imputation   
 136   XGBoost       NaN             None      Mean Imputation   
 137   XGBoost       NaN             None      Mean Imputation   
 138   XGBoost  0.618282              NaN      Mean Imputation   
 139   XGBoost  0.082055              NaN      Mean Imputation   
 140   XGBoost  0.627551              NaN      Mean Imputation   
 141   XGBoost  0.145133              NaN      Mean Imputation   
 142   XGBoost  0.659335              NaN      Mean Imputation   
 143   XGBoost  0.254467              NaN      Mean Imputation   
 144   XGBoost  0.318671              NaN      Mean Imputation   
 145   XGBoost       NaN  binary:logistic      Mean Imputation   
 146   XGBoost       NaN             None      Mean Imputation   
 147   XGBoost       NaN             None      Mean Imputation   
 148   XGBoost       NaN             None      Mean Imputation   
 149   XGBoost       NaN             None      Mean Imputation   
 150   XGBoost       NaN             None      Mean Imputation   
 151   XGBoost       NaN             None      Mean Imputation   
 152   XGBoost       NaN             None      Mean Imputation   
 153   XGBoost       NaN             None      Mean Imputation   
 154   XGBoost       NaN            False      Mean Imputation   
 155   XGBoost       NaN             None      Mean Imputation   
 156   XGBoost       NaN             None      Mean Imputation   
 157   XGBoost       NaN             None      Mean Imputation   
 158   XGBoost       NaN             None      Mean Imputation   
 159   XGBoost       NaN             None      Mean Imputation   
 160   XGBoost       NaN             None      Mean Imputation   
 161   XGBoost       NaN             None      Mean Imputation   
 162   XGBoost       NaN             None      Mean Imputation   
 163   XGBoost       NaN             None      Mean Imputation   
 164   XGBoost       NaN             None      Mean Imputation   
 165   XGBoost       NaN             None      Mean Imputation   
 166   XGBoost       NaN             None      Mean Imputation   
 167   XGBoost       NaN             None      Mean Imputation   
 168   XGBoost       NaN             None      Mean Imputation   
 169   XGBoost       NaN              NaN      Mean Imputation   
 170   XGBoost       NaN             None      Mean Imputation   
 171   XGBoost       NaN             None      Mean Imputation   
 172   XGBoost       NaN             None      Mean Imputation   
 173   XGBoost       NaN             None      Mean Imputation   
 174   XGBoost       NaN             None      Mean Imputation   
 175   XGBoost       NaN             None      Mean Imputation   
 176   XGBoost       NaN             None      Mean Imputation   
 177   XGBoost       NaN             None      Mean Imputation   
 178   XGBoost       NaN             None      Mean Imputation   
 179   XGBoost       NaN             None      Mean Imputation   
 180   XGBoost       NaN             None      Mean Imputation   
 181   XGBoost       NaN             None      Mean Imputation   
 182   XGBoost       NaN             None      Mean Imputation   
 183   XGBoost       NaN             None      Mean Imputation   
 184   XGBoost  0.635406              NaN      Mean Imputation   
 185   XGBoost  0.092748              NaN      Mean Imputation   
 186   XGBoost  0.615741              NaN      Mean Imputation   
 187   XGBoost  0.161212              NaN      Mean Imputation   
 188   XGBoost  0.677085              NaN      Mean Imputation   
 189   XGBoost  0.270717              NaN      Mean Imputation   
 190   XGBoost  0.354171              NaN      Mean Imputation   
 191   XGBoost       NaN  binary:logistic      Mean Imputation   
 192   XGBoost       NaN             None      Mean Imputation   
 193   XGBoost       NaN             None      Mean Imputation   
 194   XGBoost       NaN             None      Mean Imputation   
 195   XGBoost       NaN             None      Mean Imputation   
 196   XGBoost       NaN             None      Mean Imputation   
 197   XGBoost       NaN             None      Mean Imputation   
 198   XGBoost       NaN             None      Mean Imputation   
 199   XGBoost       NaN             None      Mean Imputation   
 200   XGBoost       NaN            False      Mean Imputation   
 201   XGBoost       NaN             None      Mean Imputation   
 202   XGBoost       NaN             None      Mean Imputation   
 203   XGBoost       NaN             None      Mean Imputation   
 204   XGBoost       NaN             None      Mean Imputation   
 205   XGBoost       NaN             None      Mean Imputation   
 206   XGBoost       NaN             None      Mean Imputation   
 207   XGBoost       NaN             None      Mean Imputation   
 208   XGBoost       NaN             None      Mean Imputation   
 209   XGBoost       NaN             None      Mean Imputation   
 210   XGBoost       NaN             None      Mean Imputation   
 211   XGBoost       NaN             None      Mean Imputation   
 212   XGBoost       NaN             None      Mean Imputation   
 213   XGBoost       NaN             None      Mean Imputation   
 214   XGBoost       NaN             None      Mean Imputation   
 215   XGBoost       NaN              NaN      Mean Imputation   
 216   XGBoost       NaN             None      Mean Imputation   
 217   XGBoost       NaN             None      Mean Imputation   
 218   XGBoost       NaN             None      Mean Imputation   
 219   XGBoost       NaN             None      Mean Imputation   
 220   XGBoost       NaN             None      Mean Imputation   
 221   XGBoost       NaN             None      Mean Imputation   
 222   XGBoost       NaN             None      Mean Imputation   
 223   XGBoost       NaN             None      Mean Imputation   
 224   XGBoost       NaN             None      Mean Imputation   
 225   XGBoost       NaN             None      Mean Imputation   
 226   XGBoost       NaN             None      Mean Imputation   
 227   XGBoost       NaN             None      Mean Imputation   
 228   XGBoost       NaN             None      Mean Imputation   
 229   XGBoost       NaN             None      Mean Imputation   
 
     Imbalance Class Technique                             Model Unique Code  
 0               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 1               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 2               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 3               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 4               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 5               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 6               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 7               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 8               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 9               Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 10              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 11              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 12              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 13              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 14              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 15              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 16              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 17              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 18              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 19              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 20              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 21              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 22              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 23              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 24              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 25              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 26              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 27              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 28              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 29              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 30              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 31              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 32              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 33              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 34              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 35              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 36              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 37              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 38              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 39              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 40              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 41              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 42              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 43              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 44              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 45              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_0  
 46              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 47              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 48              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 49              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 50              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 51              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 52              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 53              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 54              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 55              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 56              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 57              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 58              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 59              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 60              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 61              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 62              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 63              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 64              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 65              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 66              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 67              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 68              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 69              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 70              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 71              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 72              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 73              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 74              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 75              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 76              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 77              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 78              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 79              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 80              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 81              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 82              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 83              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 84              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 85              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 86              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 87              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 88              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 89              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 90              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 91              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_1  
 92              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 93              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 94              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 95              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 96              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 97              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 98              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 99              Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 100             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 101             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 102             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 103             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 104             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 105             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 106             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 107             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 108             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 109             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 110             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 111             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 112             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 113             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 114             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 115             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 116             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 117             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 118             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 119             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 120             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 121             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 122             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 123             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 124             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 125             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 126             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 127             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 128             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 129             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 130             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 131             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 132             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 133             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 134             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 135             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 136             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 137             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_2  
 138             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 139             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 140             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 141             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 142             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 143             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 144             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 145             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 146             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 147             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 148             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 149             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 150             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 151             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 152             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 153             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 154             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 155             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 156             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 157             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 158             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 159             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 160             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 161             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 162             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 163             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 164             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 165             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 166             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 167             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 168             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 169             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 170             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 171             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 172             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 173             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 174             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 175             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 176             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 177             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 178             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 179             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 180             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 181             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 182             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 183             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_3  
 184             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 185             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 186             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 187             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 188             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 189             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 190             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 191             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 192             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 193             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 194             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 195             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 196             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 197             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 198             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 199             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 200             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 201             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 202             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 203             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 204             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 205             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 206             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 207             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 208             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 209             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 210             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 211             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 212             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 213             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 214             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 215             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 216             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 217             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 218             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 219             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 220             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 221             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 222             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 223             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 224             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 225             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 226             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 227             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 228             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  
 229             Undersampling  XGBoost_Undersampling_Mean Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.647617              NaN    Median Imputation   
 1     XGBoost  0.110954              NaN    Median Imputation   
 2     XGBoost  0.662447              NaN    Median Imputation   
 3     XGBoost  0.190073              NaN    Median Imputation   
 4     XGBoost  0.689952              NaN    Median Imputation   
 5     XGBoost  0.317252              NaN    Median Imputation   
 6     XGBoost  0.379903              NaN    Median Imputation   
 7     XGBoost       NaN  binary:logistic    Median Imputation   
 8     XGBoost       NaN             None    Median Imputation   
 9     XGBoost       NaN             None    Median Imputation   
 10    XGBoost       NaN             None    Median Imputation   
 11    XGBoost       NaN             None    Median Imputation   
 12    XGBoost       NaN             None    Median Imputation   
 13    XGBoost       NaN             None    Median Imputation   
 14    XGBoost       NaN             None    Median Imputation   
 15    XGBoost       NaN             None    Median Imputation   
 16    XGBoost       NaN            False    Median Imputation   
 17    XGBoost       NaN             None    Median Imputation   
 18    XGBoost       NaN             None    Median Imputation   
 19    XGBoost       NaN             None    Median Imputation   
 20    XGBoost       NaN             None    Median Imputation   
 21    XGBoost       NaN             None    Median Imputation   
 22    XGBoost       NaN             None    Median Imputation   
 23    XGBoost       NaN             None    Median Imputation   
 24    XGBoost       NaN             None    Median Imputation   
 25    XGBoost       NaN             None    Median Imputation   
 26    XGBoost       NaN             None    Median Imputation   
 27    XGBoost       NaN             None    Median Imputation   
 28    XGBoost       NaN             None    Median Imputation   
 29    XGBoost       NaN             None    Median Imputation   
 30    XGBoost       NaN             None    Median Imputation   
 31    XGBoost       NaN              NaN    Median Imputation   
 32    XGBoost       NaN             None    Median Imputation   
 33    XGBoost       NaN             None    Median Imputation   
 34    XGBoost       NaN             None    Median Imputation   
 35    XGBoost       NaN             None    Median Imputation   
 36    XGBoost       NaN             None    Median Imputation   
 37    XGBoost       NaN             None    Median Imputation   
 38    XGBoost       NaN             None    Median Imputation   
 39    XGBoost       NaN             None    Median Imputation   
 40    XGBoost       NaN             None    Median Imputation   
 41    XGBoost       NaN             None    Median Imputation   
 42    XGBoost       NaN             None    Median Imputation   
 43    XGBoost       NaN             None    Median Imputation   
 44    XGBoost       NaN             None    Median Imputation   
 45    XGBoost       NaN             None    Median Imputation   
 46    XGBoost  0.658678              NaN    Median Imputation   
 47    XGBoost  0.074436              NaN    Median Imputation   
 48    XGBoost  0.603659              NaN    Median Imputation   
 49    XGBoost  0.132530              NaN    Median Imputation   
 50    XGBoost  0.673760              NaN    Median Imputation   
 51    XGBoost  0.291203              NaN    Median Imputation   
 52    XGBoost  0.347521              NaN    Median Imputation   
 53    XGBoost       NaN  binary:logistic    Median Imputation   
 54    XGBoost       NaN             None    Median Imputation   
 55    XGBoost       NaN             None    Median Imputation   
 56    XGBoost       NaN             None    Median Imputation   
 57    XGBoost       NaN             None    Median Imputation   
 58    XGBoost       NaN             None    Median Imputation   
 59    XGBoost       NaN             None    Median Imputation   
 60    XGBoost       NaN             None    Median Imputation   
 61    XGBoost       NaN             None    Median Imputation   
 62    XGBoost       NaN            False    Median Imputation   
 63    XGBoost       NaN             None    Median Imputation   
 64    XGBoost       NaN             None    Median Imputation   
 65    XGBoost       NaN             None    Median Imputation   
 66    XGBoost       NaN             None    Median Imputation   
 67    XGBoost       NaN             None    Median Imputation   
 68    XGBoost       NaN             None    Median Imputation   
 69    XGBoost       NaN             None    Median Imputation   
 70    XGBoost       NaN             None    Median Imputation   
 71    XGBoost       NaN             None    Median Imputation   
 72    XGBoost       NaN             None    Median Imputation   
 73    XGBoost       NaN             None    Median Imputation   
 74    XGBoost       NaN             None    Median Imputation   
 75    XGBoost       NaN             None    Median Imputation   
 76    XGBoost       NaN             None    Median Imputation   
 77    XGBoost       NaN              NaN    Median Imputation   
 78    XGBoost       NaN             None    Median Imputation   
 79    XGBoost       NaN             None    Median Imputation   
 80    XGBoost       NaN             None    Median Imputation   
 81    XGBoost       NaN             None    Median Imputation   
 82    XGBoost       NaN             None    Median Imputation   
 83    XGBoost       NaN             None    Median Imputation   
 84    XGBoost       NaN             None    Median Imputation   
 85    XGBoost       NaN             None    Median Imputation   
 86    XGBoost       NaN             None    Median Imputation   
 87    XGBoost       NaN             None    Median Imputation   
 88    XGBoost       NaN             None    Median Imputation   
 89    XGBoost       NaN             None    Median Imputation   
 90    XGBoost       NaN             None    Median Imputation   
 91    XGBoost       NaN             None    Median Imputation   
 92    XGBoost  0.625230              NaN    Median Imputation   
 93    XGBoost  0.082996              NaN    Median Imputation   
 94    XGBoost  0.657754              NaN    Median Imputation   
 95    XGBoost  0.147394              NaN    Median Imputation   
 96    XGBoost  0.677525              NaN    Median Imputation   
 97    XGBoost  0.283901              NaN    Median Imputation   
 98    XGBoost  0.355049              NaN    Median Imputation   
 99    XGBoost       NaN  binary:logistic    Median Imputation   
 100   XGBoost       NaN             None    Median Imputation   
 101   XGBoost       NaN             None    Median Imputation   
 102   XGBoost       NaN             None    Median Imputation   
 103   XGBoost       NaN             None    Median Imputation   
 104   XGBoost       NaN             None    Median Imputation   
 105   XGBoost       NaN             None    Median Imputation   
 106   XGBoost       NaN             None    Median Imputation   
 107   XGBoost       NaN             None    Median Imputation   
 108   XGBoost       NaN            False    Median Imputation   
 109   XGBoost       NaN             None    Median Imputation   
 110   XGBoost       NaN             None    Median Imputation   
 111   XGBoost       NaN             None    Median Imputation   
 112   XGBoost       NaN             None    Median Imputation   
 113   XGBoost       NaN             None    Median Imputation   
 114   XGBoost       NaN             None    Median Imputation   
 115   XGBoost       NaN             None    Median Imputation   
 116   XGBoost       NaN             None    Median Imputation   
 117   XGBoost       NaN             None    Median Imputation   
 118   XGBoost       NaN             None    Median Imputation   
 119   XGBoost       NaN             None    Median Imputation   
 120   XGBoost       NaN             None    Median Imputation   
 121   XGBoost       NaN             None    Median Imputation   
 122   XGBoost       NaN             None    Median Imputation   
 123   XGBoost       NaN              NaN    Median Imputation   
 124   XGBoost       NaN             None    Median Imputation   
 125   XGBoost       NaN             None    Median Imputation   
 126   XGBoost       NaN             None    Median Imputation   
 127   XGBoost       NaN             None    Median Imputation   
 128   XGBoost       NaN             None    Median Imputation   
 129   XGBoost       NaN             None    Median Imputation   
 130   XGBoost       NaN             None    Median Imputation   
 131   XGBoost       NaN             None    Median Imputation   
 132   XGBoost       NaN             None    Median Imputation   
 133   XGBoost       NaN             None    Median Imputation   
 134   XGBoost       NaN             None    Median Imputation   
 135   XGBoost       NaN             None    Median Imputation   
 136   XGBoost       NaN             None    Median Imputation   
 137   XGBoost       NaN             None    Median Imputation   
 138   XGBoost  0.628556              NaN    Median Imputation   
 139   XGBoost  0.086512              NaN    Median Imputation   
 140   XGBoost  0.647959              NaN    Median Imputation   
 141   XGBoost  0.152644              NaN    Median Imputation   
 142   XGBoost  0.683165              NaN    Median Imputation   
 143   XGBoost  0.296434              NaN    Median Imputation   
 144   XGBoost  0.366331              NaN    Median Imputation   
 145   XGBoost       NaN  binary:logistic    Median Imputation   
 146   XGBoost       NaN             None    Median Imputation   
 147   XGBoost       NaN             None    Median Imputation   
 148   XGBoost       NaN             None    Median Imputation   
 149   XGBoost       NaN             None    Median Imputation   
 150   XGBoost       NaN             None    Median Imputation   
 151   XGBoost       NaN             None    Median Imputation   
 152   XGBoost       NaN             None    Median Imputation   
 153   XGBoost       NaN             None    Median Imputation   
 154   XGBoost       NaN            False    Median Imputation   
 155   XGBoost       NaN             None    Median Imputation   
 156   XGBoost       NaN             None    Median Imputation   
 157   XGBoost       NaN             None    Median Imputation   
 158   XGBoost       NaN             None    Median Imputation   
 159   XGBoost       NaN             None    Median Imputation   
 160   XGBoost       NaN             None    Median Imputation   
 161   XGBoost       NaN             None    Median Imputation   
 162   XGBoost       NaN             None    Median Imputation   
 163   XGBoost       NaN             None    Median Imputation   
 164   XGBoost       NaN             None    Median Imputation   
 165   XGBoost       NaN             None    Median Imputation   
 166   XGBoost       NaN             None    Median Imputation   
 167   XGBoost       NaN             None    Median Imputation   
 168   XGBoost       NaN             None    Median Imputation   
 169   XGBoost       NaN              NaN    Median Imputation   
 170   XGBoost       NaN             None    Median Imputation   
 171   XGBoost       NaN             None    Median Imputation   
 172   XGBoost       NaN             None    Median Imputation   
 173   XGBoost       NaN             None    Median Imputation   
 174   XGBoost       NaN             None    Median Imputation   
 175   XGBoost       NaN             None    Median Imputation   
 176   XGBoost       NaN             None    Median Imputation   
 177   XGBoost       NaN             None    Median Imputation   
 178   XGBoost       NaN             None    Median Imputation   
 179   XGBoost       NaN             None    Median Imputation   
 180   XGBoost       NaN             None    Median Imputation   
 181   XGBoost       NaN             None    Median Imputation   
 182   XGBoost       NaN             None    Median Imputation   
 183   XGBoost       NaN             None    Median Imputation   
 184   XGBoost  0.638567              NaN    Median Imputation   
 185   XGBoost  0.091231              NaN    Median Imputation   
 186   XGBoost  0.597222              NaN    Median Imputation   
 187   XGBoost  0.158282              NaN    Median Imputation   
 188   XGBoost  0.682583              NaN    Median Imputation   
 189   XGBoost  0.284275              NaN    Median Imputation   
 190   XGBoost  0.365167              NaN    Median Imputation   
 191   XGBoost       NaN  binary:logistic    Median Imputation   
 192   XGBoost       NaN             None    Median Imputation   
 193   XGBoost       NaN             None    Median Imputation   
 194   XGBoost       NaN             None    Median Imputation   
 195   XGBoost       NaN             None    Median Imputation   
 196   XGBoost       NaN             None    Median Imputation   
 197   XGBoost       NaN             None    Median Imputation   
 198   XGBoost       NaN             None    Median Imputation   
 199   XGBoost       NaN             None    Median Imputation   
 200   XGBoost       NaN            False    Median Imputation   
 201   XGBoost       NaN             None    Median Imputation   
 202   XGBoost       NaN             None    Median Imputation   
 203   XGBoost       NaN             None    Median Imputation   
 204   XGBoost       NaN             None    Median Imputation   
 205   XGBoost       NaN             None    Median Imputation   
 206   XGBoost       NaN             None    Median Imputation   
 207   XGBoost       NaN             None    Median Imputation   
 208   XGBoost       NaN             None    Median Imputation   
 209   XGBoost       NaN             None    Median Imputation   
 210   XGBoost       NaN             None    Median Imputation   
 211   XGBoost       NaN             None    Median Imputation   
 212   XGBoost       NaN             None    Median Imputation   
 213   XGBoost       NaN             None    Median Imputation   
 214   XGBoost       NaN             None    Median Imputation   
 215   XGBoost       NaN              NaN    Median Imputation   
 216   XGBoost       NaN             None    Median Imputation   
 217   XGBoost       NaN             None    Median Imputation   
 218   XGBoost       NaN             None    Median Imputation   
 219   XGBoost       NaN             None    Median Imputation   
 220   XGBoost       NaN             None    Median Imputation   
 221   XGBoost       NaN             None    Median Imputation   
 222   XGBoost       NaN             None    Median Imputation   
 223   XGBoost       NaN             None    Median Imputation   
 224   XGBoost       NaN             None    Median Imputation   
 225   XGBoost       NaN             None    Median Imputation   
 226   XGBoost       NaN             None    Median Imputation   
 227   XGBoost       NaN             None    Median Imputation   
 228   XGBoost       NaN             None    Median Imputation   
 229   XGBoost       NaN             None    Median Imputation   
 
     Imbalance Class Technique                               Model Unique Code  
 0               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 1               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 2               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 3               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 4               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 5               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 6               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 7               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 8               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 9               Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 10              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 11              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 12              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 13              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 14              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 15              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 16              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 17              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 18              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 19              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 20              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 21              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 22              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 23              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 24              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 25              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 26              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 27              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 28              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 29              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 30              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 31              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 32              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 33              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 34              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 35              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 36              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 37              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 38              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 39              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 40              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 41              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 42              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 43              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 44              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 45              Undersampling  XGBoost_Undersampling_Median Imputation_fold_0  
 46              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 47              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 48              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 49              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 50              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 51              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 52              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 53              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 54              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 55              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 56              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 57              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 58              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 59              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 60              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 61              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 62              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 63              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 64              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 65              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 66              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 67              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 68              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 69              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 70              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 71              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 72              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 73              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 74              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 75              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 76              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 77              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 78              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 79              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 80              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 81              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 82              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 83              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 84              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 85              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 86              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 87              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 88              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 89              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 90              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 91              Undersampling  XGBoost_Undersampling_Median Imputation_fold_1  
 92              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 93              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 94              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 95              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 96              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 97              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 98              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 99              Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 100             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 101             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 102             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 103             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 104             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 105             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 106             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 107             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 108             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 109             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 110             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 111             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 112             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 113             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 114             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 115             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 116             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 117             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 118             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 119             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 120             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 121             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 122             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 123             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 124             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 125             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 126             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 127             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 128             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 129             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 130             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 131             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 132             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 133             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 134             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 135             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 136             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 137             Undersampling  XGBoost_Undersampling_Median Imputation_fold_2  
 138             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 139             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 140             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 141             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 142             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 143             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 144             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 145             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 146             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 147             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 148             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 149             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 150             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 151             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 152             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 153             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 154             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 155             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 156             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 157             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 158             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 159             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 160             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 161             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 162             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 163             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 164             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 165             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 166             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 167             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 168             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 169             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 170             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 171             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 172             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 173             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 174             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 175             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 176             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 177             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 178             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 179             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 180             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 181             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 182             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 183             Undersampling  XGBoost_Undersampling_Median Imputation_fold_3  
 184             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 185             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 186             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 187             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 188             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 189             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 190             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 191             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 192             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 193             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 194             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 195             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 196             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 197             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 198             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 199             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 200             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 201             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 202             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 203             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 204             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 205             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 206             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 207             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 208             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 209             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 210             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 211             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 212             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 213             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 214             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 215             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 216             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 217             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 218             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 219             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 220             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 221             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 222             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 223             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 224             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 225             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 226             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 227             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 228             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  
 229             Undersampling  XGBoost_Undersampling_Median Imputation_fold_4  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.880169              NaN      Zero Imputation   
 1     XGBoost  0.131757              NaN      Zero Imputation   
 2     XGBoost  0.164557              NaN      Zero Imputation   
 3     XGBoost  0.146341              NaN      Zero Imputation   
 4     XGBoost  0.683441              NaN      Zero Imputation   
 5     XGBoost  0.301803              NaN      Zero Imputation   
 6     XGBoost  0.366882              NaN      Zero Imputation   
 7     XGBoost       NaN  binary:logistic      Zero Imputation   
 8     XGBoost       NaN             None      Zero Imputation   
 9     XGBoost       NaN             None      Zero Imputation   
 10    XGBoost       NaN             None      Zero Imputation   
 11    XGBoost       NaN             None      Zero Imputation   
 12    XGBoost       NaN             None      Zero Imputation   
 13    XGBoost       NaN             None      Zero Imputation   
 14    XGBoost       NaN             None      Zero Imputation   
 15    XGBoost       NaN             None      Zero Imputation   
 16    XGBoost       NaN            False      Zero Imputation   
 17    XGBoost       NaN             None      Zero Imputation   
 18    XGBoost       NaN             None      Zero Imputation   
 19    XGBoost       NaN             None      Zero Imputation   
 20    XGBoost       NaN             None      Zero Imputation   
 21    XGBoost       NaN             None      Zero Imputation   
 22    XGBoost       NaN             None      Zero Imputation   
 23    XGBoost       NaN             None      Zero Imputation   
 24    XGBoost       NaN             None      Zero Imputation   
 25    XGBoost       NaN             None      Zero Imputation   
 26    XGBoost       NaN             None      Zero Imputation   
 27    XGBoost       NaN             None      Zero Imputation   
 28    XGBoost       NaN             None      Zero Imputation   
 29    XGBoost       NaN             None      Zero Imputation   
 30    XGBoost       NaN             None      Zero Imputation   
 31    XGBoost       NaN              NaN      Zero Imputation   
 32    XGBoost       NaN             None      Zero Imputation   
 33    XGBoost       NaN             None      Zero Imputation   
 34    XGBoost       NaN             None      Zero Imputation   
 35    XGBoost       NaN             None      Zero Imputation   
 36    XGBoost       NaN             None      Zero Imputation   
 37    XGBoost       NaN             None      Zero Imputation   
 38    XGBoost       NaN             None      Zero Imputation   
 39    XGBoost       NaN             None      Zero Imputation   
 40    XGBoost       NaN             None      Zero Imputation   
 41    XGBoost       NaN             None      Zero Imputation   
 42    XGBoost       NaN             None      Zero Imputation   
 43    XGBoost       NaN             None      Zero Imputation   
 44    XGBoost       NaN             None      Zero Imputation   
 45    XGBoost       NaN             None      Zero Imputation   
 46    XGBoost  0.894390              NaN      Zero Imputation   
 47    XGBoost  0.114007              NaN      Zero Imputation   
 48    XGBoost  0.213415              NaN      Zero Imputation   
 49    XGBoost  0.148620              NaN      Zero Imputation   
 50    XGBoost  0.706000              NaN      Zero Imputation   
 51    XGBoost  0.345082              NaN      Zero Imputation   
 52    XGBoost  0.411999              NaN      Zero Imputation   
 53    XGBoost       NaN  binary:logistic      Zero Imputation   
 54    XGBoost       NaN             None      Zero Imputation   
 55    XGBoost       NaN             None      Zero Imputation   
 56    XGBoost       NaN             None      Zero Imputation   
 57    XGBoost       NaN             None      Zero Imputation   
 58    XGBoost       NaN             None      Zero Imputation   
 59    XGBoost       NaN             None      Zero Imputation   
 60    XGBoost       NaN             None      Zero Imputation   
 61    XGBoost       NaN             None      Zero Imputation   
 62    XGBoost       NaN            False      Zero Imputation   
 63    XGBoost       NaN             None      Zero Imputation   
 64    XGBoost       NaN             None      Zero Imputation   
 65    XGBoost       NaN             None      Zero Imputation   
 66    XGBoost       NaN             None      Zero Imputation   
 67    XGBoost       NaN             None      Zero Imputation   
 68    XGBoost       NaN             None      Zero Imputation   
 69    XGBoost       NaN             None      Zero Imputation   
 70    XGBoost       NaN             None      Zero Imputation   
 71    XGBoost       NaN             None      Zero Imputation   
 72    XGBoost       NaN             None      Zero Imputation   
 73    XGBoost       NaN             None      Zero Imputation   
 74    XGBoost       NaN             None      Zero Imputation   
 75    XGBoost       NaN             None      Zero Imputation   
 76    XGBoost       NaN             None      Zero Imputation   
 77    XGBoost       NaN              NaN      Zero Imputation   
 78    XGBoost       NaN             None      Zero Imputation   
 79    XGBoost       NaN             None      Zero Imputation   
 80    XGBoost       NaN             None      Zero Imputation   
 81    XGBoost       NaN             None      Zero Imputation   
 82    XGBoost       NaN             None      Zero Imputation   
 83    XGBoost       NaN             None      Zero Imputation   
 84    XGBoost       NaN             None      Zero Imputation   
 85    XGBoost       NaN             None      Zero Imputation   
 86    XGBoost       NaN             None      Zero Imputation   
 87    XGBoost       NaN             None      Zero Imputation   
 88    XGBoost       NaN             None      Zero Imputation   
 89    XGBoost       NaN             None      Zero Imputation   
 90    XGBoost       NaN             None      Zero Imputation   
 91    XGBoost       NaN             None      Zero Imputation   
 92    XGBoost  0.891757              NaN      Zero Imputation   
 93    XGBoost  0.119048              NaN      Zero Imputation   
 94    XGBoost  0.187166              NaN      Zero Imputation   
 95    XGBoost  0.145530              NaN      Zero Imputation   
 96    XGBoost  0.673546              NaN      Zero Imputation   
 97    XGBoost  0.274377              NaN      Zero Imputation   
 98    XGBoost  0.347091              NaN      Zero Imputation   
 99    XGBoost       NaN  binary:logistic      Zero Imputation   
 100   XGBoost       NaN             None      Zero Imputation   
 101   XGBoost       NaN             None      Zero Imputation   
 102   XGBoost       NaN             None      Zero Imputation   
 103   XGBoost       NaN             None      Zero Imputation   
 104   XGBoost       NaN             None      Zero Imputation   
 105   XGBoost       NaN             None      Zero Imputation   
 106   XGBoost       NaN             None      Zero Imputation   
 107   XGBoost       NaN             None      Zero Imputation   
 108   XGBoost       NaN            False      Zero Imputation   
 109   XGBoost       NaN             None      Zero Imputation   
 110   XGBoost       NaN             None      Zero Imputation   
 111   XGBoost       NaN             None      Zero Imputation   
 112   XGBoost       NaN             None      Zero Imputation   
 113   XGBoost       NaN             None      Zero Imputation   
 114   XGBoost       NaN             None      Zero Imputation   
 115   XGBoost       NaN             None      Zero Imputation   
 116   XGBoost       NaN             None      Zero Imputation   
 117   XGBoost       NaN             None      Zero Imputation   
 118   XGBoost       NaN             None      Zero Imputation   
 119   XGBoost       NaN             None      Zero Imputation   
 120   XGBoost       NaN             None      Zero Imputation   
 121   XGBoost       NaN             None      Zero Imputation   
 122   XGBoost       NaN             None      Zero Imputation   
 123   XGBoost       NaN              NaN      Zero Imputation   
 124   XGBoost       NaN             None      Zero Imputation   
 125   XGBoost       NaN             None      Zero Imputation   
 126   XGBoost       NaN             None      Zero Imputation   
 127   XGBoost       NaN             None      Zero Imputation   
 128   XGBoost       NaN             None      Zero Imputation   
 129   XGBoost       NaN             None      Zero Imputation   
 130   XGBoost       NaN             None      Zero Imputation   
 131   XGBoost       NaN             None      Zero Imputation   
 132   XGBoost       NaN             None      Zero Imputation   
 133   XGBoost       NaN             None      Zero Imputation   
 134   XGBoost       NaN             None      Zero Imputation   
 135   XGBoost       NaN             None      Zero Imputation   
 136   XGBoost       NaN             None      Zero Imputation   
 137   XGBoost       NaN             None      Zero Imputation   
 138   XGBoost  0.891992              NaN      Zero Imputation   
 139   XGBoost  0.133562              NaN      Zero Imputation   
 140   XGBoost  0.198980              NaN      Zero Imputation   
 141   XGBoost  0.159836              NaN      Zero Imputation   
 142   XGBoost  0.654897              NaN      Zero Imputation   
 143   XGBoost  0.218271              NaN      Zero Imputation   
 144   XGBoost  0.309795              NaN      Zero Imputation   
 145   XGBoost       NaN  binary:logistic      Zero Imputation   
 146   XGBoost       NaN             None      Zero Imputation   
 147   XGBoost       NaN             None      Zero Imputation   
 148   XGBoost       NaN             None      Zero Imputation   
 149   XGBoost       NaN             None      Zero Imputation   
 150   XGBoost       NaN             None      Zero Imputation   
 151   XGBoost       NaN             None      Zero Imputation   
 152   XGBoost       NaN             None      Zero Imputation   
 153   XGBoost       NaN             None      Zero Imputation   
 154   XGBoost       NaN            False      Zero Imputation   
 155   XGBoost       NaN             None      Zero Imputation   
 156   XGBoost       NaN             None      Zero Imputation   
 157   XGBoost       NaN             None      Zero Imputation   
 158   XGBoost       NaN             None      Zero Imputation   
 159   XGBoost       NaN             None      Zero Imputation   
 160   XGBoost       NaN             None      Zero Imputation   
 161   XGBoost       NaN             None      Zero Imputation   
 162   XGBoost       NaN             None      Zero Imputation   
 163   XGBoost       NaN             None      Zero Imputation   
 164   XGBoost       NaN             None      Zero Imputation   
 165   XGBoost       NaN             None      Zero Imputation   
 166   XGBoost       NaN             None      Zero Imputation   
 167   XGBoost       NaN             None      Zero Imputation   
 168   XGBoost       NaN             None      Zero Imputation   
 169   XGBoost       NaN              NaN      Zero Imputation   
 170   XGBoost       NaN             None      Zero Imputation   
 171   XGBoost       NaN             None      Zero Imputation   
 172   XGBoost       NaN             None      Zero Imputation   
 173   XGBoost       NaN             None      Zero Imputation   
 174   XGBoost       NaN             None      Zero Imputation   
 175   XGBoost       NaN             None      Zero Imputation   
 176   XGBoost       NaN             None      Zero Imputation   
 177   XGBoost       NaN             None      Zero Imputation   
 178   XGBoost       NaN             None      Zero Imputation   
 179   XGBoost       NaN             None      Zero Imputation   
 180   XGBoost       NaN             None      Zero Imputation   
 181   XGBoost       NaN             None      Zero Imputation   
 182   XGBoost       NaN             None      Zero Imputation   
 183   XGBoost       NaN             None      Zero Imputation   
 184   XGBoost  0.896733              NaN      Zero Imputation   
 185   XGBoost  0.169173              NaN      Zero Imputation   
 186   XGBoost  0.208333              NaN      Zero Imputation   
 187   XGBoost  0.186722              NaN      Zero Imputation   
 188   XGBoost  0.708842              NaN      Zero Imputation   
 189   XGBoost  0.342122              NaN      Zero Imputation   
 190   XGBoost  0.417684              NaN      Zero Imputation   
 191   XGBoost       NaN  binary:logistic      Zero Imputation   
 192   XGBoost       NaN             None      Zero Imputation   
 193   XGBoost       NaN             None      Zero Imputation   
 194   XGBoost       NaN             None      Zero Imputation   
 195   XGBoost       NaN             None      Zero Imputation   
 196   XGBoost       NaN             None      Zero Imputation   
 197   XGBoost       NaN             None      Zero Imputation   
 198   XGBoost       NaN             None      Zero Imputation   
 199   XGBoost       NaN             None      Zero Imputation   
 200   XGBoost       NaN            False      Zero Imputation   
 201   XGBoost       NaN             None      Zero Imputation   
 202   XGBoost       NaN             None      Zero Imputation   
 203   XGBoost       NaN             None      Zero Imputation   
 204   XGBoost       NaN             None      Zero Imputation   
 205   XGBoost       NaN             None      Zero Imputation   
 206   XGBoost       NaN             None      Zero Imputation   
 207   XGBoost       NaN             None      Zero Imputation   
 208   XGBoost       NaN             None      Zero Imputation   
 209   XGBoost       NaN             None      Zero Imputation   
 210   XGBoost       NaN             None      Zero Imputation   
 211   XGBoost       NaN             None      Zero Imputation   
 212   XGBoost       NaN             None      Zero Imputation   
 213   XGBoost       NaN             None      Zero Imputation   
 214   XGBoost       NaN             None      Zero Imputation   
 215   XGBoost       NaN              NaN      Zero Imputation   
 216   XGBoost       NaN             None      Zero Imputation   
 217   XGBoost       NaN             None      Zero Imputation   
 218   XGBoost       NaN             None      Zero Imputation   
 219   XGBoost       NaN             None      Zero Imputation   
 220   XGBoost       NaN             None      Zero Imputation   
 221   XGBoost       NaN             None      Zero Imputation   
 222   XGBoost       NaN             None      Zero Imputation   
 223   XGBoost       NaN             None      Zero Imputation   
 224   XGBoost       NaN             None      Zero Imputation   
 225   XGBoost       NaN             None      Zero Imputation   
 226   XGBoost       NaN             None      Zero Imputation   
 227   XGBoost       NaN             None      Zero Imputation   
 228   XGBoost       NaN             None      Zero Imputation   
 229   XGBoost       NaN             None      Zero Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 150  Hybrid Sampling (SMOTEENN)   
 151  Hybrid Sampling (SMOTEENN)   
 152  Hybrid Sampling (SMOTEENN)   
 153  Hybrid Sampling (SMOTEENN)   
 154  Hybrid Sampling (SMOTEENN)   
 155  Hybrid Sampling (SMOTEENN)   
 156  Hybrid Sampling (SMOTEENN)   
 157  Hybrid Sampling (SMOTEENN)   
 158  Hybrid Sampling (SMOTEENN)   
 159  Hybrid Sampling (SMOTEENN)   
 160  Hybrid Sampling (SMOTEENN)   
 161  Hybrid Sampling (SMOTEENN)   
 162  Hybrid Sampling (SMOTEENN)   
 163  Hybrid Sampling (SMOTEENN)   
 164  Hybrid Sampling (SMOTEENN)   
 165  Hybrid Sampling (SMOTEENN)   
 166  Hybrid Sampling (SMOTEENN)   
 167  Hybrid Sampling (SMOTEENN)   
 168  Hybrid Sampling (SMOTEENN)   
 169  Hybrid Sampling (SMOTEENN)   
 170  Hybrid Sampling (SMOTEENN)   
 171  Hybrid Sampling (SMOTEENN)   
 172  Hybrid Sampling (SMOTEENN)   
 173  Hybrid Sampling (SMOTEENN)   
 174  Hybrid Sampling (SMOTEENN)   
 175  Hybrid Sampling (SMOTEENN)   
 176  Hybrid Sampling (SMOTEENN)   
 177  Hybrid Sampling (SMOTEENN)   
 178  Hybrid Sampling (SMOTEENN)   
 179  Hybrid Sampling (SMOTEENN)   
 180  Hybrid Sampling (SMOTEENN)   
 181  Hybrid Sampling (SMOTEENN)   
 182  Hybrid Sampling (SMOTEENN)   
 183  Hybrid Sampling (SMOTEENN)   
 184  Hybrid Sampling (SMOTEENN)   
 185  Hybrid Sampling (SMOTEENN)   
 186  Hybrid Sampling (SMOTEENN)   
 187  Hybrid Sampling (SMOTEENN)   
 188  Hybrid Sampling (SMOTEENN)   
 189  Hybrid Sampling (SMOTEENN)   
 190  Hybrid Sampling (SMOTEENN)   
 191  Hybrid Sampling (SMOTEENN)   
 192  Hybrid Sampling (SMOTEENN)   
 193  Hybrid Sampling (SMOTEENN)   
 194  Hybrid Sampling (SMOTEENN)   
 195  Hybrid Sampling (SMOTEENN)   
 196  Hybrid Sampling (SMOTEENN)   
 197  Hybrid Sampling (SMOTEENN)   
 198  Hybrid Sampling (SMOTEENN)   
 199  Hybrid Sampling (SMOTEENN)   
 200  Hybrid Sampling (SMOTEENN)   
 201  Hybrid Sampling (SMOTEENN)   
 202  Hybrid Sampling (SMOTEENN)   
 203  Hybrid Sampling (SMOTEENN)   
 204  Hybrid Sampling (SMOTEENN)   
 205  Hybrid Sampling (SMOTEENN)   
 206  Hybrid Sampling (SMOTEENN)   
 207  Hybrid Sampling (SMOTEENN)   
 208  Hybrid Sampling (SMOTEENN)   
 209  Hybrid Sampling (SMOTEENN)   
 210  Hybrid Sampling (SMOTEENN)   
 211  Hybrid Sampling (SMOTEENN)   
 212  Hybrid Sampling (SMOTEENN)   
 213  Hybrid Sampling (SMOTEENN)   
 214  Hybrid Sampling (SMOTEENN)   
 215  Hybrid Sampling (SMOTEENN)   
 216  Hybrid Sampling (SMOTEENN)   
 217  Hybrid Sampling (SMOTEENN)   
 218  Hybrid Sampling (SMOTEENN)   
 219  Hybrid Sampling (SMOTEENN)   
 220  Hybrid Sampling (SMOTEENN)   
 221  Hybrid Sampling (SMOTEENN)   
 222  Hybrid Sampling (SMOTEENN)   
 223  Hybrid Sampling (SMOTEENN)   
 224  Hybrid Sampling (SMOTEENN)   
 225  Hybrid Sampling (SMOTEENN)   
 226  Hybrid Sampling (SMOTEENN)   
 227  Hybrid Sampling (SMOTEENN)   
 228  Hybrid Sampling (SMOTEENN)   
 229  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 1    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 2    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 3    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 4    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 5    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 6    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 7    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 8    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 9    XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 10   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 11   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 12   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 13   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 14   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 15   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 16   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 17   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 18   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 19   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 20   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 21   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 22   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 23   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 24   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 25   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 26   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 27   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 28   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 29   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 30   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 31   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 32   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 33   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 34   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 35   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 36   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 37   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 38   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 39   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 40   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 41   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 42   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 43   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 44   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 45   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 46   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 47   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 48   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 49   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 50   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 51   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 52   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 53   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 54   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 55   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 56   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 57   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 58   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 59   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 60   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 61   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 62   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 63   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 64   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 65   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 66   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 67   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 68   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 69   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 70   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 71   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 72   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 73   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 74   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 75   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 76   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 77   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 78   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 79   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 80   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 81   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 82   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 83   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 84   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 85   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 86   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 87   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 88   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 89   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 90   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 91   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 92   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 93   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 94   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 95   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 96   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 97   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 98   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 99   XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 100  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 101  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 102  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 103  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 104  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 105  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 106  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 107  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 108  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 109  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 110  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 111  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 112  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 113  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 114  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 115  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 116  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 117  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 118  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 119  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 120  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 121  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 122  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 123  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 124  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 125  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 126  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 127  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 128  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 129  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 130  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 131  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 132  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 133  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 134  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 135  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 136  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 137  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 138  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 139  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 140  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 141  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 142  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 143  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 144  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 145  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 146  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 147  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 148  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 149  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 150  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 151  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 152  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 153  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 154  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 155  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 156  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 157  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 158  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 159  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 160  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 161  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 162  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 163  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 164  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 165  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 166  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 167  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 168  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 169  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 170  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 171  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 172  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 173  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 174  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 175  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 176  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 177  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 178  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 179  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 180  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 181  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 182  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 183  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 184  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 185  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 186  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 187  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 188  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 189  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 190  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 191  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 192  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 193  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 194  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 195  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 196  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 197  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 198  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 199  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 200  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 201  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 202  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 203  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 204  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 205  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 206  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 207  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 208  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 209  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 210  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 211  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 212  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 213  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 214  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 215  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 216  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 217  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 218  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 219  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 220  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 221  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 222  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 223  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 224  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 225  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 226  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 227  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 228  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  
 229  XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.894917              NaN      Mean Imputation   
 1     XGBoost  0.141593              NaN      Mean Imputation   
 2     XGBoost  0.135021              NaN      Mean Imputation   
 3     XGBoost  0.138229              NaN      Mean Imputation   
 4     XGBoost  0.673920              NaN      Mean Imputation   
 5     XGBoost  0.260803              NaN      Mean Imputation   
 6     XGBoost  0.347841              NaN      Mean Imputation   
 7     XGBoost       NaN  binary:logistic      Mean Imputation   
 8     XGBoost       NaN             None      Mean Imputation   
 9     XGBoost       NaN             None      Mean Imputation   
 10    XGBoost       NaN             None      Mean Imputation   
 11    XGBoost       NaN             None      Mean Imputation   
 12    XGBoost       NaN             None      Mean Imputation   
 13    XGBoost       NaN             None      Mean Imputation   
 14    XGBoost       NaN             None      Mean Imputation   
 15    XGBoost       NaN             None      Mean Imputation   
 16    XGBoost       NaN            False      Mean Imputation   
 17    XGBoost       NaN             None      Mean Imputation   
 18    XGBoost       NaN             None      Mean Imputation   
 19    XGBoost       NaN             None      Mean Imputation   
 20    XGBoost       NaN             None      Mean Imputation   
 21    XGBoost       NaN             None      Mean Imputation   
 22    XGBoost       NaN             None      Mean Imputation   
 23    XGBoost       NaN             None      Mean Imputation   
 24    XGBoost       NaN             None      Mean Imputation   
 25    XGBoost       NaN             None      Mean Imputation   
 26    XGBoost       NaN             None      Mean Imputation   
 27    XGBoost       NaN             None      Mean Imputation   
 28    XGBoost       NaN             None      Mean Imputation   
 29    XGBoost       NaN             None      Mean Imputation   
 30    XGBoost       NaN             None      Mean Imputation   
 31    XGBoost       NaN              NaN      Mean Imputation   
 32    XGBoost       NaN             None      Mean Imputation   
 33    XGBoost       NaN             None      Mean Imputation   
 34    XGBoost       NaN             None      Mean Imputation   
 35    XGBoost       NaN             None      Mean Imputation   
 36    XGBoost       NaN             None      Mean Imputation   
 37    XGBoost       NaN             None      Mean Imputation   
 38    XGBoost       NaN             None      Mean Imputation   
 39    XGBoost       NaN             None      Mean Imputation   
 40    XGBoost       NaN             None      Mean Imputation   
 41    XGBoost       NaN             None      Mean Imputation   
 42    XGBoost       NaN             None      Mean Imputation   
 43    XGBoost       NaN             None      Mean Imputation   
 44    XGBoost       NaN             None      Mean Imputation   
 45    XGBoost       NaN             None      Mean Imputation   
 46    XGBoost  0.902291              NaN      Mean Imputation   
 47    XGBoost  0.129032              NaN      Mean Imputation   
 48    XGBoost  0.219512              NaN      Mean Imputation   
 49    XGBoost  0.162528              NaN      Mean Imputation   
 50    XGBoost  0.680034              NaN      Mean Imputation   
 51    XGBoost  0.310942              NaN      Mean Imputation   
 52    XGBoost  0.360068              NaN      Mean Imputation   
 53    XGBoost       NaN  binary:logistic      Mean Imputation   
 54    XGBoost       NaN             None      Mean Imputation   
 55    XGBoost       NaN             None      Mean Imputation   
 56    XGBoost       NaN             None      Mean Imputation   
 57    XGBoost       NaN             None      Mean Imputation   
 58    XGBoost       NaN             None      Mean Imputation   
 59    XGBoost       NaN             None      Mean Imputation   
 60    XGBoost       NaN             None      Mean Imputation   
 61    XGBoost       NaN             None      Mean Imputation   
 62    XGBoost       NaN            False      Mean Imputation   
 63    XGBoost       NaN             None      Mean Imputation   
 64    XGBoost       NaN             None      Mean Imputation   
 65    XGBoost       NaN             None      Mean Imputation   
 66    XGBoost       NaN             None      Mean Imputation   
 67    XGBoost       NaN             None      Mean Imputation   
 68    XGBoost       NaN             None      Mean Imputation   
 69    XGBoost       NaN             None      Mean Imputation   
 70    XGBoost       NaN             None      Mean Imputation   
 71    XGBoost       NaN             None      Mean Imputation   
 72    XGBoost       NaN             None      Mean Imputation   
 73    XGBoost       NaN             None      Mean Imputation   
 74    XGBoost       NaN             None      Mean Imputation   
 75    XGBoost       NaN             None      Mean Imputation   
 76    XGBoost       NaN             None      Mean Imputation   
 77    XGBoost       NaN              NaN      Mean Imputation   
 78    XGBoost       NaN             None      Mean Imputation   
 79    XGBoost       NaN             None      Mean Imputation   
 80    XGBoost       NaN             None      Mean Imputation   
 81    XGBoost       NaN             None      Mean Imputation   
 82    XGBoost       NaN             None      Mean Imputation   
 83    XGBoost       NaN             None      Mean Imputation   
 84    XGBoost       NaN             None      Mean Imputation   
 85    XGBoost       NaN             None      Mean Imputation   
 86    XGBoost       NaN             None      Mean Imputation   
 87    XGBoost       NaN             None      Mean Imputation   
 88    XGBoost       NaN             None      Mean Imputation   
 89    XGBoost       NaN             None      Mean Imputation   
 90    XGBoost       NaN             None      Mean Imputation   
 91    XGBoost       NaN             None      Mean Imputation   
 92    XGBoost  0.886226              NaN      Mean Imputation   
 93    XGBoost  0.103560              NaN      Mean Imputation   
 94    XGBoost  0.171123              NaN      Mean Imputation   
 95    XGBoost  0.129032              NaN      Mean Imputation   
 96    XGBoost  0.657033              NaN      Mean Imputation   
 97    XGBoost  0.252343              NaN      Mean Imputation   
 98    XGBoost  0.314065              NaN      Mean Imputation   
 99    XGBoost       NaN  binary:logistic      Mean Imputation   
 100   XGBoost       NaN             None      Mean Imputation   
 101   XGBoost       NaN             None      Mean Imputation   
 102   XGBoost       NaN             None      Mean Imputation   
 103   XGBoost       NaN             None      Mean Imputation   
 104   XGBoost       NaN             None      Mean Imputation   
 105   XGBoost       NaN             None      Mean Imputation   
 106   XGBoost       NaN             None      Mean Imputation   
 107   XGBoost       NaN             None      Mean Imputation   
 108   XGBoost       NaN            False      Mean Imputation   
 109   XGBoost       NaN             None      Mean Imputation   
 110   XGBoost       NaN             None      Mean Imputation   
 111   XGBoost       NaN             None      Mean Imputation   
 112   XGBoost       NaN             None      Mean Imputation   
 113   XGBoost       NaN             None      Mean Imputation   
 114   XGBoost       NaN             None      Mean Imputation   
 115   XGBoost       NaN             None      Mean Imputation   
 116   XGBoost       NaN             None      Mean Imputation   
 117   XGBoost       NaN             None      Mean Imputation   
 118   XGBoost       NaN             None      Mean Imputation   
 119   XGBoost       NaN             None      Mean Imputation   
 120   XGBoost       NaN             None      Mean Imputation   
 121   XGBoost       NaN             None      Mean Imputation   
 122   XGBoost       NaN             None      Mean Imputation   
 123   XGBoost       NaN              NaN      Mean Imputation   
 124   XGBoost       NaN             None      Mean Imputation   
 125   XGBoost       NaN             None      Mean Imputation   
 126   XGBoost       NaN             None      Mean Imputation   
 127   XGBoost       NaN             None      Mean Imputation   
 128   XGBoost       NaN             None      Mean Imputation   
 129   XGBoost       NaN             None      Mean Imputation   
 130   XGBoost       NaN             None      Mean Imputation   
 131   XGBoost       NaN             None      Mean Imputation   
 132   XGBoost       NaN             None      Mean Imputation   
 133   XGBoost       NaN             None      Mean Imputation   
 134   XGBoost       NaN             None      Mean Imputation   
 135   XGBoost       NaN             None      Mean Imputation   
 136   XGBoost       NaN             None      Mean Imputation   
 137   XGBoost       NaN             None      Mean Imputation   
 138   XGBoost  0.893836              NaN      Mean Imputation   
 139   XGBoost  0.129032              NaN      Mean Imputation   
 140   XGBoost  0.183673              NaN      Mean Imputation   
 141   XGBoost  0.151579              NaN      Mean Imputation   
 142   XGBoost  0.666504              NaN      Mean Imputation   
 143   XGBoost  0.288906              NaN      Mean Imputation   
 144   XGBoost  0.333007              NaN      Mean Imputation   
 145   XGBoost       NaN  binary:logistic      Mean Imputation   
 146   XGBoost       NaN             None      Mean Imputation   
 147   XGBoost       NaN             None      Mean Imputation   
 148   XGBoost       NaN             None      Mean Imputation   
 149   XGBoost       NaN             None      Mean Imputation   
 150   XGBoost       NaN             None      Mean Imputation   
 151   XGBoost       NaN             None      Mean Imputation   
 152   XGBoost       NaN             None      Mean Imputation   
 153   XGBoost       NaN             None      Mean Imputation   
 154   XGBoost       NaN            False      Mean Imputation   
 155   XGBoost       NaN             None      Mean Imputation   
 156   XGBoost       NaN             None      Mean Imputation   
 157   XGBoost       NaN             None      Mean Imputation   
 158   XGBoost       NaN             None      Mean Imputation   
 159   XGBoost       NaN             None      Mean Imputation   
 160   XGBoost       NaN             None      Mean Imputation   
 161   XGBoost       NaN             None      Mean Imputation   
 162   XGBoost       NaN             None      Mean Imputation   
 163   XGBoost       NaN             None      Mean Imputation   
 164   XGBoost       NaN             None      Mean Imputation   
 165   XGBoost       NaN             None      Mean Imputation   
 166   XGBoost       NaN             None      Mean Imputation   
 167   XGBoost       NaN             None      Mean Imputation   
 168   XGBoost       NaN             None      Mean Imputation   
 169   XGBoost       NaN              NaN      Mean Imputation   
 170   XGBoost       NaN             None      Mean Imputation   
 171   XGBoost       NaN             None      Mean Imputation   
 172   XGBoost       NaN             None      Mean Imputation   
 173   XGBoost       NaN             None      Mean Imputation   
 174   XGBoost       NaN             None      Mean Imputation   
 175   XGBoost       NaN             None      Mean Imputation   
 176   XGBoost       NaN             None      Mean Imputation   
 177   XGBoost       NaN             None      Mean Imputation   
 178   XGBoost       NaN             None      Mean Imputation   
 179   XGBoost       NaN             None      Mean Imputation   
 180   XGBoost       NaN             None      Mean Imputation   
 181   XGBoost       NaN             None      Mean Imputation   
 182   XGBoost       NaN             None      Mean Imputation   
 183   XGBoost       NaN             None      Mean Imputation   
 184   XGBoost  0.892518              NaN      Mean Imputation   
 185   XGBoost  0.139098              NaN      Mean Imputation   
 186   XGBoost  0.171296              NaN      Mean Imputation   
 187   XGBoost  0.153527              NaN      Mean Imputation   
 188   XGBoost  0.664519              NaN      Mean Imputation   
 189   XGBoost  0.246250              NaN      Mean Imputation   
 190   XGBoost  0.329037              NaN      Mean Imputation   
 191   XGBoost       NaN  binary:logistic      Mean Imputation   
 192   XGBoost       NaN             None      Mean Imputation   
 193   XGBoost       NaN             None      Mean Imputation   
 194   XGBoost       NaN             None      Mean Imputation   
 195   XGBoost       NaN             None      Mean Imputation   
 196   XGBoost       NaN             None      Mean Imputation   
 197   XGBoost       NaN             None      Mean Imputation   
 198   XGBoost       NaN             None      Mean Imputation   
 199   XGBoost       NaN             None      Mean Imputation   
 200   XGBoost       NaN            False      Mean Imputation   
 201   XGBoost       NaN             None      Mean Imputation   
 202   XGBoost       NaN             None      Mean Imputation   
 203   XGBoost       NaN             None      Mean Imputation   
 204   XGBoost       NaN             None      Mean Imputation   
 205   XGBoost       NaN             None      Mean Imputation   
 206   XGBoost       NaN             None      Mean Imputation   
 207   XGBoost       NaN             None      Mean Imputation   
 208   XGBoost       NaN             None      Mean Imputation   
 209   XGBoost       NaN             None      Mean Imputation   
 210   XGBoost       NaN             None      Mean Imputation   
 211   XGBoost       NaN             None      Mean Imputation   
 212   XGBoost       NaN             None      Mean Imputation   
 213   XGBoost       NaN             None      Mean Imputation   
 214   XGBoost       NaN             None      Mean Imputation   
 215   XGBoost       NaN              NaN      Mean Imputation   
 216   XGBoost       NaN             None      Mean Imputation   
 217   XGBoost       NaN             None      Mean Imputation   
 218   XGBoost       NaN             None      Mean Imputation   
 219   XGBoost       NaN             None      Mean Imputation   
 220   XGBoost       NaN             None      Mean Imputation   
 221   XGBoost       NaN             None      Mean Imputation   
 222   XGBoost       NaN             None      Mean Imputation   
 223   XGBoost       NaN             None      Mean Imputation   
 224   XGBoost       NaN             None      Mean Imputation   
 225   XGBoost       NaN             None      Mean Imputation   
 226   XGBoost       NaN             None      Mean Imputation   
 227   XGBoost       NaN             None      Mean Imputation   
 228   XGBoost       NaN             None      Mean Imputation   
 229   XGBoost       NaN             None      Mean Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 150  Hybrid Sampling (SMOTEENN)   
 151  Hybrid Sampling (SMOTEENN)   
 152  Hybrid Sampling (SMOTEENN)   
 153  Hybrid Sampling (SMOTEENN)   
 154  Hybrid Sampling (SMOTEENN)   
 155  Hybrid Sampling (SMOTEENN)   
 156  Hybrid Sampling (SMOTEENN)   
 157  Hybrid Sampling (SMOTEENN)   
 158  Hybrid Sampling (SMOTEENN)   
 159  Hybrid Sampling (SMOTEENN)   
 160  Hybrid Sampling (SMOTEENN)   
 161  Hybrid Sampling (SMOTEENN)   
 162  Hybrid Sampling (SMOTEENN)   
 163  Hybrid Sampling (SMOTEENN)   
 164  Hybrid Sampling (SMOTEENN)   
 165  Hybrid Sampling (SMOTEENN)   
 166  Hybrid Sampling (SMOTEENN)   
 167  Hybrid Sampling (SMOTEENN)   
 168  Hybrid Sampling (SMOTEENN)   
 169  Hybrid Sampling (SMOTEENN)   
 170  Hybrid Sampling (SMOTEENN)   
 171  Hybrid Sampling (SMOTEENN)   
 172  Hybrid Sampling (SMOTEENN)   
 173  Hybrid Sampling (SMOTEENN)   
 174  Hybrid Sampling (SMOTEENN)   
 175  Hybrid Sampling (SMOTEENN)   
 176  Hybrid Sampling (SMOTEENN)   
 177  Hybrid Sampling (SMOTEENN)   
 178  Hybrid Sampling (SMOTEENN)   
 179  Hybrid Sampling (SMOTEENN)   
 180  Hybrid Sampling (SMOTEENN)   
 181  Hybrid Sampling (SMOTEENN)   
 182  Hybrid Sampling (SMOTEENN)   
 183  Hybrid Sampling (SMOTEENN)   
 184  Hybrid Sampling (SMOTEENN)   
 185  Hybrid Sampling (SMOTEENN)   
 186  Hybrid Sampling (SMOTEENN)   
 187  Hybrid Sampling (SMOTEENN)   
 188  Hybrid Sampling (SMOTEENN)   
 189  Hybrid Sampling (SMOTEENN)   
 190  Hybrid Sampling (SMOTEENN)   
 191  Hybrid Sampling (SMOTEENN)   
 192  Hybrid Sampling (SMOTEENN)   
 193  Hybrid Sampling (SMOTEENN)   
 194  Hybrid Sampling (SMOTEENN)   
 195  Hybrid Sampling (SMOTEENN)   
 196  Hybrid Sampling (SMOTEENN)   
 197  Hybrid Sampling (SMOTEENN)   
 198  Hybrid Sampling (SMOTEENN)   
 199  Hybrid Sampling (SMOTEENN)   
 200  Hybrid Sampling (SMOTEENN)   
 201  Hybrid Sampling (SMOTEENN)   
 202  Hybrid Sampling (SMOTEENN)   
 203  Hybrid Sampling (SMOTEENN)   
 204  Hybrid Sampling (SMOTEENN)   
 205  Hybrid Sampling (SMOTEENN)   
 206  Hybrid Sampling (SMOTEENN)   
 207  Hybrid Sampling (SMOTEENN)   
 208  Hybrid Sampling (SMOTEENN)   
 209  Hybrid Sampling (SMOTEENN)   
 210  Hybrid Sampling (SMOTEENN)   
 211  Hybrid Sampling (SMOTEENN)   
 212  Hybrid Sampling (SMOTEENN)   
 213  Hybrid Sampling (SMOTEENN)   
 214  Hybrid Sampling (SMOTEENN)   
 215  Hybrid Sampling (SMOTEENN)   
 216  Hybrid Sampling (SMOTEENN)   
 217  Hybrid Sampling (SMOTEENN)   
 218  Hybrid Sampling (SMOTEENN)   
 219  Hybrid Sampling (SMOTEENN)   
 220  Hybrid Sampling (SMOTEENN)   
 221  Hybrid Sampling (SMOTEENN)   
 222  Hybrid Sampling (SMOTEENN)   
 223  Hybrid Sampling (SMOTEENN)   
 224  Hybrid Sampling (SMOTEENN)   
 225  Hybrid Sampling (SMOTEENN)   
 226  Hybrid Sampling (SMOTEENN)   
 227  Hybrid Sampling (SMOTEENN)   
 228  Hybrid Sampling (SMOTEENN)   
 229  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 1    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 2    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 3    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 4    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 5    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 6    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 7    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 8    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 9    XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 10   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 11   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 12   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 13   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 14   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 15   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 16   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 17   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 18   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 19   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 20   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 21   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 22   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 23   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 24   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 25   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 26   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 27   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 28   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 29   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 30   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 31   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 32   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 33   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 34   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 35   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 36   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 37   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 38   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 39   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 40   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 41   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 42   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 43   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 44   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 45   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 46   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 47   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 48   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 49   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 50   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 51   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 52   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 53   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 54   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 55   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 56   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 57   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 58   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 59   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 60   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 61   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 62   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 63   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 64   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 65   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 66   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 67   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 68   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 69   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 70   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 71   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 72   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 73   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 74   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 75   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 76   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 77   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 78   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 79   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 80   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 81   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 82   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 83   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 84   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 85   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 86   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 87   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 88   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 89   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 90   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 91   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 92   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 93   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 94   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 95   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 96   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 97   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 98   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 99   XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 100  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 101  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 102  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 103  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 104  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 105  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 106  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 107  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 108  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 109  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 110  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 111  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 112  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 113  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 114  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 115  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 116  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 117  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 118  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 119  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 120  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 121  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 122  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 123  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 124  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 125  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 126  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 127  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 128  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 129  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 130  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 131  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 132  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 133  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 134  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 135  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 136  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 137  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 138  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 139  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 140  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 141  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 142  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 143  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 144  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 145  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 146  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 147  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 148  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 149  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 150  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 151  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 152  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 153  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 154  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 155  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 156  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 157  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 158  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 159  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 160  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 161  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 162  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 163  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 164  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 165  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 166  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 167  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 168  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 169  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 170  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 171  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 172  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 173  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 174  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 175  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 176  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 177  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 178  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 179  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 180  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 181  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 182  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 183  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 184  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 185  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 186  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 187  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 188  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 189  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 190  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 191  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 192  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 193  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 194  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 195  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 196  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 197  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 198  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 199  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 200  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 201  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 202  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 203  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 204  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 205  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 206  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 207  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 208  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 209  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 210  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 211  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 212  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 213  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 214  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 215  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 216  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 217  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 218  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 219  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 220  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 221  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 222  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 223  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 224  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 225  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 226  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 227  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 228  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  
 229  XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.890703              NaN    Median Imputation   
 1     XGBoost  0.132231              NaN    Median Imputation   
 2     XGBoost  0.135021              NaN    Median Imputation   
 3     XGBoost  0.133612              NaN    Median Imputation   
 4     XGBoost  0.661779              NaN    Median Imputation   
 5     XGBoost  0.253255              NaN    Median Imputation   
 6     XGBoost  0.323558              NaN    Median Imputation   
 7     XGBoost       NaN  binary:logistic    Median Imputation   
 8     XGBoost       NaN             None    Median Imputation   
 9     XGBoost       NaN             None    Median Imputation   
 10    XGBoost       NaN             None    Median Imputation   
 11    XGBoost       NaN             None    Median Imputation   
 12    XGBoost       NaN             None    Median Imputation   
 13    XGBoost       NaN             None    Median Imputation   
 14    XGBoost       NaN             None    Median Imputation   
 15    XGBoost       NaN             None    Median Imputation   
 16    XGBoost       NaN            False    Median Imputation   
 17    XGBoost       NaN             None    Median Imputation   
 18    XGBoost       NaN             None    Median Imputation   
 19    XGBoost       NaN             None    Median Imputation   
 20    XGBoost       NaN             None    Median Imputation   
 21    XGBoost       NaN             None    Median Imputation   
 22    XGBoost       NaN             None    Median Imputation   
 23    XGBoost       NaN             None    Median Imputation   
 24    XGBoost       NaN             None    Median Imputation   
 25    XGBoost       NaN             None    Median Imputation   
 26    XGBoost       NaN             None    Median Imputation   
 27    XGBoost       NaN             None    Median Imputation   
 28    XGBoost       NaN             None    Median Imputation   
 29    XGBoost       NaN             None    Median Imputation   
 30    XGBoost       NaN             None    Median Imputation   
 31    XGBoost       NaN              NaN    Median Imputation   
 32    XGBoost       NaN             None    Median Imputation   
 33    XGBoost       NaN             None    Median Imputation   
 34    XGBoost       NaN             None    Median Imputation   
 35    XGBoost       NaN             None    Median Imputation   
 36    XGBoost       NaN             None    Median Imputation   
 37    XGBoost       NaN             None    Median Imputation   
 38    XGBoost       NaN             None    Median Imputation   
 39    XGBoost       NaN             None    Median Imputation   
 40    XGBoost       NaN             None    Median Imputation   
 41    XGBoost       NaN             None    Median Imputation   
 42    XGBoost       NaN             None    Median Imputation   
 43    XGBoost       NaN             None    Median Imputation   
 44    XGBoost       NaN             None    Median Imputation   
 45    XGBoost       NaN             None    Median Imputation   
 46    XGBoost  0.893337              NaN    Median Imputation   
 47    XGBoost  0.091525              NaN    Median Imputation   
 48    XGBoost  0.164634              NaN    Median Imputation   
 49    XGBoost  0.117647              NaN    Median Imputation   
 50    XGBoost  0.659554              NaN    Median Imputation   
 51    XGBoost  0.268086              NaN    Median Imputation   
 52    XGBoost  0.319107              NaN    Median Imputation   
 53    XGBoost       NaN  binary:logistic    Median Imputation   
 54    XGBoost       NaN             None    Median Imputation   
 55    XGBoost       NaN             None    Median Imputation   
 56    XGBoost       NaN             None    Median Imputation   
 57    XGBoost       NaN             None    Median Imputation   
 58    XGBoost       NaN             None    Median Imputation   
 59    XGBoost       NaN             None    Median Imputation   
 60    XGBoost       NaN             None    Median Imputation   
 61    XGBoost       NaN             None    Median Imputation   
 62    XGBoost       NaN            False    Median Imputation   
 63    XGBoost       NaN             None    Median Imputation   
 64    XGBoost       NaN             None    Median Imputation   
 65    XGBoost       NaN             None    Median Imputation   
 66    XGBoost       NaN             None    Median Imputation   
 67    XGBoost       NaN             None    Median Imputation   
 68    XGBoost       NaN             None    Median Imputation   
 69    XGBoost       NaN             None    Median Imputation   
 70    XGBoost       NaN             None    Median Imputation   
 71    XGBoost       NaN             None    Median Imputation   
 72    XGBoost       NaN             None    Median Imputation   
 73    XGBoost       NaN             None    Median Imputation   
 74    XGBoost       NaN             None    Median Imputation   
 75    XGBoost       NaN             None    Median Imputation   
 76    XGBoost       NaN             None    Median Imputation   
 77    XGBoost       NaN              NaN    Median Imputation   
 78    XGBoost       NaN             None    Median Imputation   
 79    XGBoost       NaN             None    Median Imputation   
 80    XGBoost       NaN             None    Median Imputation   
 81    XGBoost       NaN             None    Median Imputation   
 82    XGBoost       NaN             None    Median Imputation   
 83    XGBoost       NaN             None    Median Imputation   
 84    XGBoost       NaN             None    Median Imputation   
 85    XGBoost       NaN             None    Median Imputation   
 86    XGBoost       NaN             None    Median Imputation   
 87    XGBoost       NaN             None    Median Imputation   
 88    XGBoost       NaN             None    Median Imputation   
 89    XGBoost       NaN             None    Median Imputation   
 90    XGBoost       NaN             None    Median Imputation   
 91    XGBoost       NaN             None    Median Imputation   
 92    XGBoost  0.894390              NaN    Median Imputation   
 93    XGBoost  0.125874              NaN    Median Imputation   
 94    XGBoost  0.192513              NaN    Median Imputation   
 95    XGBoost  0.152220              NaN    Median Imputation   
 96    XGBoost  0.672655              NaN    Median Imputation   
 97    XGBoost  0.255957              NaN    Median Imputation   
 98    XGBoost  0.345311              NaN    Median Imputation   
 99    XGBoost       NaN  binary:logistic    Median Imputation   
 100   XGBoost       NaN             None    Median Imputation   
 101   XGBoost       NaN             None    Median Imputation   
 102   XGBoost       NaN             None    Median Imputation   
 103   XGBoost       NaN             None    Median Imputation   
 104   XGBoost       NaN             None    Median Imputation   
 105   XGBoost       NaN             None    Median Imputation   
 106   XGBoost       NaN             None    Median Imputation   
 107   XGBoost       NaN             None    Median Imputation   
 108   XGBoost       NaN            False    Median Imputation   
 109   XGBoost       NaN             None    Median Imputation   
 110   XGBoost       NaN             None    Median Imputation   
 111   XGBoost       NaN             None    Median Imputation   
 112   XGBoost       NaN             None    Median Imputation   
 113   XGBoost       NaN             None    Median Imputation   
 114   XGBoost       NaN             None    Median Imputation   
 115   XGBoost       NaN             None    Median Imputation   
 116   XGBoost       NaN             None    Median Imputation   
 117   XGBoost       NaN             None    Median Imputation   
 118   XGBoost       NaN             None    Median Imputation   
 119   XGBoost       NaN             None    Median Imputation   
 120   XGBoost       NaN             None    Median Imputation   
 121   XGBoost       NaN             None    Median Imputation   
 122   XGBoost       NaN             None    Median Imputation   
 123   XGBoost       NaN              NaN    Median Imputation   
 124   XGBoost       NaN             None    Median Imputation   
 125   XGBoost       NaN             None    Median Imputation   
 126   XGBoost       NaN             None    Median Imputation   
 127   XGBoost       NaN             None    Median Imputation   
 128   XGBoost       NaN             None    Median Imputation   
 129   XGBoost       NaN             None    Median Imputation   
 130   XGBoost       NaN             None    Median Imputation   
 131   XGBoost       NaN             None    Median Imputation   
 132   XGBoost       NaN             None    Median Imputation   
 133   XGBoost       NaN             None    Median Imputation   
 134   XGBoost       NaN             None    Median Imputation   
 135   XGBoost       NaN             None    Median Imputation   
 136   XGBoost       NaN             None    Median Imputation   
 137   XGBoost       NaN             None    Median Imputation   
 138   XGBoost  0.886459              NaN    Median Imputation   
 139   XGBoost  0.117264              NaN    Median Imputation   
 140   XGBoost  0.183673              NaN    Median Imputation   
 141   XGBoost  0.143141              NaN    Median Imputation   
 142   XGBoost  0.652479              NaN    Median Imputation   
 143   XGBoost  0.254031              NaN    Median Imputation   
 144   XGBoost  0.304959              NaN    Median Imputation   
 145   XGBoost       NaN  binary:logistic    Median Imputation   
 146   XGBoost       NaN             None    Median Imputation   
 147   XGBoost       NaN             None    Median Imputation   
 148   XGBoost       NaN             None    Median Imputation   
 149   XGBoost       NaN             None    Median Imputation   
 150   XGBoost       NaN             None    Median Imputation   
 151   XGBoost       NaN             None    Median Imputation   
 152   XGBoost       NaN             None    Median Imputation   
 153   XGBoost       NaN             None    Median Imputation   
 154   XGBoost       NaN            False    Median Imputation   
 155   XGBoost       NaN             None    Median Imputation   
 156   XGBoost       NaN             None    Median Imputation   
 157   XGBoost       NaN             None    Median Imputation   
 158   XGBoost       NaN             None    Median Imputation   
 159   XGBoost       NaN             None    Median Imputation   
 160   XGBoost       NaN             None    Median Imputation   
 161   XGBoost       NaN             None    Median Imputation   
 162   XGBoost       NaN             None    Median Imputation   
 163   XGBoost       NaN             None    Median Imputation   
 164   XGBoost       NaN             None    Median Imputation   
 165   XGBoost       NaN             None    Median Imputation   
 166   XGBoost       NaN             None    Median Imputation   
 167   XGBoost       NaN             None    Median Imputation   
 168   XGBoost       NaN             None    Median Imputation   
 169   XGBoost       NaN              NaN    Median Imputation   
 170   XGBoost       NaN             None    Median Imputation   
 171   XGBoost       NaN             None    Median Imputation   
 172   XGBoost       NaN             None    Median Imputation   
 173   XGBoost       NaN             None    Median Imputation   
 174   XGBoost       NaN             None    Median Imputation   
 175   XGBoost       NaN             None    Median Imputation   
 176   XGBoost       NaN             None    Median Imputation   
 177   XGBoost       NaN             None    Median Imputation   
 178   XGBoost       NaN             None    Median Imputation   
 179   XGBoost       NaN             None    Median Imputation   
 180   XGBoost       NaN             None    Median Imputation   
 181   XGBoost       NaN             None    Median Imputation   
 182   XGBoost       NaN             None    Median Imputation   
 183   XGBoost       NaN             None    Median Imputation   
 184   XGBoost  0.890938              NaN    Median Imputation   
 185   XGBoost  0.148936              NaN    Median Imputation   
 186   XGBoost  0.194444              NaN    Median Imputation   
 187   XGBoost  0.168675              NaN    Median Imputation   
 188   XGBoost  0.686883              NaN    Median Imputation   
 189   XGBoost  0.292075              NaN    Median Imputation   
 190   XGBoost  0.373766              NaN    Median Imputation   
 191   XGBoost       NaN  binary:logistic    Median Imputation   
 192   XGBoost       NaN             None    Median Imputation   
 193   XGBoost       NaN             None    Median Imputation   
 194   XGBoost       NaN             None    Median Imputation   
 195   XGBoost       NaN             None    Median Imputation   
 196   XGBoost       NaN             None    Median Imputation   
 197   XGBoost       NaN             None    Median Imputation   
 198   XGBoost       NaN             None    Median Imputation   
 199   XGBoost       NaN             None    Median Imputation   
 200   XGBoost       NaN            False    Median Imputation   
 201   XGBoost       NaN             None    Median Imputation   
 202   XGBoost       NaN             None    Median Imputation   
 203   XGBoost       NaN             None    Median Imputation   
 204   XGBoost       NaN             None    Median Imputation   
 205   XGBoost       NaN             None    Median Imputation   
 206   XGBoost       NaN             None    Median Imputation   
 207   XGBoost       NaN             None    Median Imputation   
 208   XGBoost       NaN             None    Median Imputation   
 209   XGBoost       NaN             None    Median Imputation   
 210   XGBoost       NaN             None    Median Imputation   
 211   XGBoost       NaN             None    Median Imputation   
 212   XGBoost       NaN             None    Median Imputation   
 213   XGBoost       NaN             None    Median Imputation   
 214   XGBoost       NaN             None    Median Imputation   
 215   XGBoost       NaN              NaN    Median Imputation   
 216   XGBoost       NaN             None    Median Imputation   
 217   XGBoost       NaN             None    Median Imputation   
 218   XGBoost       NaN             None    Median Imputation   
 219   XGBoost       NaN             None    Median Imputation   
 220   XGBoost       NaN             None    Median Imputation   
 221   XGBoost       NaN             None    Median Imputation   
 222   XGBoost       NaN             None    Median Imputation   
 223   XGBoost       NaN             None    Median Imputation   
 224   XGBoost       NaN             None    Median Imputation   
 225   XGBoost       NaN             None    Median Imputation   
 226   XGBoost       NaN             None    Median Imputation   
 227   XGBoost       NaN             None    Median Imputation   
 228   XGBoost       NaN             None    Median Imputation   
 229   XGBoost       NaN             None    Median Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 130  Hybrid Sampling (SMOTEENN)   
 131  Hybrid Sampling (SMOTEENN)   
 132  Hybrid Sampling (SMOTEENN)   
 133  Hybrid Sampling (SMOTEENN)   
 134  Hybrid Sampling (SMOTEENN)   
 135  Hybrid Sampling (SMOTEENN)   
 136  Hybrid Sampling (SMOTEENN)   
 137  Hybrid Sampling (SMOTEENN)   
 138  Hybrid Sampling (SMOTEENN)   
 139  Hybrid Sampling (SMOTEENN)   
 140  Hybrid Sampling (SMOTEENN)   
 141  Hybrid Sampling (SMOTEENN)   
 142  Hybrid Sampling (SMOTEENN)   
 143  Hybrid Sampling (SMOTEENN)   
 144  Hybrid Sampling (SMOTEENN)   
 145  Hybrid Sampling (SMOTEENN)   
 146  Hybrid Sampling (SMOTEENN)   
 147  Hybrid Sampling (SMOTEENN)   
 148  Hybrid Sampling (SMOTEENN)   
 149  Hybrid Sampling (SMOTEENN)   
 150  Hybrid Sampling (SMOTEENN)   
 151  Hybrid Sampling (SMOTEENN)   
 152  Hybrid Sampling (SMOTEENN)   
 153  Hybrid Sampling (SMOTEENN)   
 154  Hybrid Sampling (SMOTEENN)   
 155  Hybrid Sampling (SMOTEENN)   
 156  Hybrid Sampling (SMOTEENN)   
 157  Hybrid Sampling (SMOTEENN)   
 158  Hybrid Sampling (SMOTEENN)   
 159  Hybrid Sampling (SMOTEENN)   
 160  Hybrid Sampling (SMOTEENN)   
 161  Hybrid Sampling (SMOTEENN)   
 162  Hybrid Sampling (SMOTEENN)   
 163  Hybrid Sampling (SMOTEENN)   
 164  Hybrid Sampling (SMOTEENN)   
 165  Hybrid Sampling (SMOTEENN)   
 166  Hybrid Sampling (SMOTEENN)   
 167  Hybrid Sampling (SMOTEENN)   
 168  Hybrid Sampling (SMOTEENN)   
 169  Hybrid Sampling (SMOTEENN)   
 170  Hybrid Sampling (SMOTEENN)   
 171  Hybrid Sampling (SMOTEENN)   
 172  Hybrid Sampling (SMOTEENN)   
 173  Hybrid Sampling (SMOTEENN)   
 174  Hybrid Sampling (SMOTEENN)   
 175  Hybrid Sampling (SMOTEENN)   
 176  Hybrid Sampling (SMOTEENN)   
 177  Hybrid Sampling (SMOTEENN)   
 178  Hybrid Sampling (SMOTEENN)   
 179  Hybrid Sampling (SMOTEENN)   
 180  Hybrid Sampling (SMOTEENN)   
 181  Hybrid Sampling (SMOTEENN)   
 182  Hybrid Sampling (SMOTEENN)   
 183  Hybrid Sampling (SMOTEENN)   
 184  Hybrid Sampling (SMOTEENN)   
 185  Hybrid Sampling (SMOTEENN)   
 186  Hybrid Sampling (SMOTEENN)   
 187  Hybrid Sampling (SMOTEENN)   
 188  Hybrid Sampling (SMOTEENN)   
 189  Hybrid Sampling (SMOTEENN)   
 190  Hybrid Sampling (SMOTEENN)   
 191  Hybrid Sampling (SMOTEENN)   
 192  Hybrid Sampling (SMOTEENN)   
 193  Hybrid Sampling (SMOTEENN)   
 194  Hybrid Sampling (SMOTEENN)   
 195  Hybrid Sampling (SMOTEENN)   
 196  Hybrid Sampling (SMOTEENN)   
 197  Hybrid Sampling (SMOTEENN)   
 198  Hybrid Sampling (SMOTEENN)   
 199  Hybrid Sampling (SMOTEENN)   
 200  Hybrid Sampling (SMOTEENN)   
 201  Hybrid Sampling (SMOTEENN)   
 202  Hybrid Sampling (SMOTEENN)   
 203  Hybrid Sampling (SMOTEENN)   
 204  Hybrid Sampling (SMOTEENN)   
 205  Hybrid Sampling (SMOTEENN)   
 206  Hybrid Sampling (SMOTEENN)   
 207  Hybrid Sampling (SMOTEENN)   
 208  Hybrid Sampling (SMOTEENN)   
 209  Hybrid Sampling (SMOTEENN)   
 210  Hybrid Sampling (SMOTEENN)   
 211  Hybrid Sampling (SMOTEENN)   
 212  Hybrid Sampling (SMOTEENN)   
 213  Hybrid Sampling (SMOTEENN)   
 214  Hybrid Sampling (SMOTEENN)   
 215  Hybrid Sampling (SMOTEENN)   
 216  Hybrid Sampling (SMOTEENN)   
 217  Hybrid Sampling (SMOTEENN)   
 218  Hybrid Sampling (SMOTEENN)   
 219  Hybrid Sampling (SMOTEENN)   
 220  Hybrid Sampling (SMOTEENN)   
 221  Hybrid Sampling (SMOTEENN)   
 222  Hybrid Sampling (SMOTEENN)   
 223  Hybrid Sampling (SMOTEENN)   
 224  Hybrid Sampling (SMOTEENN)   
 225  Hybrid Sampling (SMOTEENN)   
 226  Hybrid Sampling (SMOTEENN)   
 227  Hybrid Sampling (SMOTEENN)   
 228  Hybrid Sampling (SMOTEENN)   
 229  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 1    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 2    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 3    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 4    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 5    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 6    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 7    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 8    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 9    XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 10   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 11   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 12   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 13   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 14   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 15   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 16   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 17   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 18   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 19   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 20   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 21   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 22   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 23   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 24   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 25   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 26   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 27   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 28   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 29   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 30   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 31   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 32   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 33   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 34   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 35   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 36   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 37   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 38   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 39   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 40   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 41   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 42   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 43   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 44   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 45   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 46   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 47   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 48   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 49   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 50   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 51   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 52   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 53   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 54   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 55   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 56   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 57   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 58   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 59   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 60   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 61   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 62   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 63   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 64   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 65   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 66   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 67   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 68   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 69   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 70   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 71   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 72   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 73   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 74   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 75   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 76   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 77   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 78   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 79   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 80   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 81   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 82   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 83   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 84   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 85   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 86   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 87   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 88   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 89   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 90   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 91   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 92   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 93   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 94   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 95   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 96   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 97   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 98   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 99   XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 100  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 101  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 102  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 103  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 104  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 105  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 106  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 107  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 108  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 109  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 110  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 111  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 112  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 113  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 114  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 115  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 116  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 117  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 118  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 119  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 120  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 121  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 122  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 123  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 124  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 125  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 126  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 127  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 128  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 129  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 130  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 131  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 132  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 133  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 134  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 135  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 136  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 137  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 138  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 139  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 140  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 141  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 142  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 143  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 144  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 145  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 146  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 147  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 148  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 149  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 150  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 151  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 152  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 153  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 154  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 155  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 156  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 157  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 158  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 159  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 160  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 161  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 162  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 163  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 164  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 165  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 166  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 167  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 168  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 169  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 170  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 171  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 172  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 173  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 174  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 175  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 176  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 177  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 178  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 179  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 180  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 181  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 182  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 183  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 184  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 185  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 186  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 187  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 188  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 189  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 190  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 191  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 192  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 193  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 194  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 195  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 196  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 197  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 198  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 199  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 200  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 201  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 202  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 203  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 204  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 205  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 206  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 207  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 208  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 209  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 210  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 211  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 212  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 213  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 214  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 215  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 216  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 217  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 218  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 219  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 220  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 221  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 222  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 223  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 224  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 225  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 226  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 227  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 228  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  
 229  XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.889913              NaN      Zero Imputation   
 1     XGBoost  0.127572              NaN      Zero Imputation   
 2     XGBoost  0.130802              NaN      Zero Imputation   
 3     XGBoost  0.129167              NaN      Zero Imputation   
 4     XGBoost  0.668105              NaN      Zero Imputation   
 5     XGBoost  0.282391              NaN      Zero Imputation   
 6     XGBoost  0.336210              NaN      Zero Imputation   
 7     XGBoost       NaN  binary:logistic      Zero Imputation   
 8     XGBoost       NaN             None      Zero Imputation   
 9     XGBoost       NaN             None      Zero Imputation   
 10    XGBoost       NaN             None      Zero Imputation   
 11    XGBoost       NaN             None      Zero Imputation   
 12    XGBoost       NaN             None      Zero Imputation   
 13    XGBoost       NaN             None      Zero Imputation   
 14    XGBoost       NaN             None      Zero Imputation   
 15    XGBoost       NaN             None      Zero Imputation   
 16    XGBoost       NaN            False      Zero Imputation   
 17    XGBoost       NaN             None      Zero Imputation   
 18    XGBoost       NaN             None      Zero Imputation   
 19    XGBoost       NaN             None      Zero Imputation   
 20    XGBoost       NaN             None      Zero Imputation   
 21    XGBoost       NaN             None      Zero Imputation   
 22    XGBoost       NaN             None      Zero Imputation   
 23    XGBoost       NaN             None      Zero Imputation   
 24    XGBoost       NaN             None      Zero Imputation   
 25    XGBoost       NaN             None      Zero Imputation   
 26    XGBoost       NaN             None      Zero Imputation   
 27    XGBoost       NaN             None      Zero Imputation   
 28    XGBoost       NaN             None      Zero Imputation   
 29    XGBoost       NaN             None      Zero Imputation   
 30    XGBoost       NaN             None      Zero Imputation   
 31    XGBoost       NaN              NaN      Zero Imputation   
 32    XGBoost       NaN             None      Zero Imputation   
 33    XGBoost       NaN             None      Zero Imputation   
 34    XGBoost       NaN             None      Zero Imputation   
 35    XGBoost       NaN             None      Zero Imputation   
 36    XGBoost       NaN             None      Zero Imputation   
 37    XGBoost       NaN             None      Zero Imputation   
 38    XGBoost       NaN             None      Zero Imputation   
 39    XGBoost       NaN             None      Zero Imputation   
 40    XGBoost       NaN             None      Zero Imputation   
 41    XGBoost       NaN             None      Zero Imputation   
 42    XGBoost       NaN             None      Zero Imputation   
 43    XGBoost       NaN             None      Zero Imputation   
 44    XGBoost       NaN             None      Zero Imputation   
 45    XGBoost       NaN             None      Zero Imputation   
 46    XGBoost  0.903871              NaN      Zero Imputation   
 47    XGBoost  0.131868              NaN      Zero Imputation   
 48    XGBoost  0.219512              NaN      Zero Imputation   
 49    XGBoost  0.164760              NaN      Zero Imputation   
 50    XGBoost  0.695321              NaN      Zero Imputation   
 51    XGBoost  0.296330              NaN      Zero Imputation   
 52    XGBoost  0.390642              NaN      Zero Imputation   
 53    XGBoost       NaN  binary:logistic      Zero Imputation   
 54    XGBoost       NaN             None      Zero Imputation   
 55    XGBoost       NaN             None      Zero Imputation   
 56    XGBoost       NaN             None      Zero Imputation   
 57    XGBoost       NaN             None      Zero Imputation   
 58    XGBoost       NaN             None      Zero Imputation   
 59    XGBoost       NaN             None      Zero Imputation   
 60    XGBoost       NaN             None      Zero Imputation   
 61    XGBoost       NaN             None      Zero Imputation   
 62    XGBoost       NaN            False      Zero Imputation   
 63    XGBoost       NaN             None      Zero Imputation   
 64    XGBoost       NaN             None      Zero Imputation   
 65    XGBoost       NaN             None      Zero Imputation   
 66    XGBoost       NaN             None      Zero Imputation   
 67    XGBoost       NaN             None      Zero Imputation   
 68    XGBoost       NaN             None      Zero Imputation   
 69    XGBoost       NaN             None      Zero Imputation   
 70    XGBoost       NaN             None      Zero Imputation   
 71    XGBoost       NaN             None      Zero Imputation   
 72    XGBoost       NaN             None      Zero Imputation   
 73    XGBoost       NaN             None      Zero Imputation   
 74    XGBoost       NaN             None      Zero Imputation   
 75    XGBoost       NaN             None      Zero Imputation   
 76    XGBoost       NaN             None      Zero Imputation   
 77    XGBoost       NaN              NaN      Zero Imputation   
 78    XGBoost       NaN             None      Zero Imputation   
 79    XGBoost       NaN             None      Zero Imputation   
 80    XGBoost       NaN             None      Zero Imputation   
 81    XGBoost       NaN             None      Zero Imputation   
 82    XGBoost       NaN             None      Zero Imputation   
 83    XGBoost       NaN             None      Zero Imputation   
 84    XGBoost       NaN             None      Zero Imputation   
 85    XGBoost       NaN             None      Zero Imputation   
 86    XGBoost       NaN             None      Zero Imputation   
 87    XGBoost       NaN             None      Zero Imputation   
 88    XGBoost       NaN             None      Zero Imputation   
 89    XGBoost       NaN             None      Zero Imputation   
 90    XGBoost       NaN             None      Zero Imputation   
 91    XGBoost       NaN             None      Zero Imputation   
 92    XGBoost  0.900184              NaN      Zero Imputation   
 93    XGBoost  0.136364              NaN      Zero Imputation   
 94    XGBoost  0.192513              NaN      Zero Imputation   
 95    XGBoost  0.159645              NaN      Zero Imputation   
 96    XGBoost  0.686458              NaN      Zero Imputation   
 97    XGBoost  0.284813              NaN      Zero Imputation   
 98    XGBoost  0.372915              NaN      Zero Imputation   
 99    XGBoost       NaN  binary:logistic      Zero Imputation   
 100   XGBoost       NaN             None      Zero Imputation   
 101   XGBoost       NaN             None      Zero Imputation   
 102   XGBoost       NaN             None      Zero Imputation   
 103   XGBoost       NaN             None      Zero Imputation   
 104   XGBoost       NaN             None      Zero Imputation   
 105   XGBoost       NaN             None      Zero Imputation   
 106   XGBoost       NaN             None      Zero Imputation   
 107   XGBoost       NaN             None      Zero Imputation   
 108   XGBoost       NaN            False      Zero Imputation   
 109   XGBoost       NaN             None      Zero Imputation   
 110   XGBoost       NaN             None      Zero Imputation   
 111   XGBoost       NaN             None      Zero Imputation   
 112   XGBoost       NaN             None      Zero Imputation   
 113   XGBoost       NaN             None      Zero Imputation   
 114   XGBoost       NaN             None      Zero Imputation   
 115   XGBoost       NaN             None      Zero Imputation   
 116   XGBoost       NaN             None      Zero Imputation   
 117   XGBoost       NaN             None      Zero Imputation   
 118   XGBoost       NaN             None      Zero Imputation   
 119   XGBoost       NaN             None      Zero Imputation   
 120   XGBoost       NaN             None      Zero Imputation   
 121   XGBoost       NaN             None      Zero Imputation   
 122   XGBoost       NaN             None      Zero Imputation   
 123   XGBoost       NaN              NaN      Zero Imputation   
 124   XGBoost       NaN             None      Zero Imputation   
 125   XGBoost       NaN             None      Zero Imputation   
 126   XGBoost       NaN             None      Zero Imputation   
 127   XGBoost       NaN             None      Zero Imputation   
 128   XGBoost       NaN             None      Zero Imputation   
 129   XGBoost       NaN             None      Zero Imputation   
 130   XGBoost       NaN             None      Zero Imputation   
 131   XGBoost       NaN             None      Zero Imputation   
 132   XGBoost       NaN             None      Zero Imputation   
 133   XGBoost       NaN             None      Zero Imputation   
 134   XGBoost       NaN             None      Zero Imputation   
 135   XGBoost       NaN             None      Zero Imputation   
 136   XGBoost       NaN             None      Zero Imputation   
 137   XGBoost       NaN             None      Zero Imputation   
 138   XGBoost  0.897524              NaN      Zero Imputation   
 139   XGBoost  0.118577              NaN      Zero Imputation   
 140   XGBoost  0.153061              NaN      Zero Imputation   
 141   XGBoost  0.133630              NaN      Zero Imputation   
 142   XGBoost  0.671283              NaN      Zero Imputation   
 143   XGBoost  0.263730              NaN      Zero Imputation   
 144   XGBoost  0.342567              NaN      Zero Imputation   
 145   XGBoost       NaN  binary:logistic      Zero Imputation   
 146   XGBoost       NaN             None      Zero Imputation   
 147   XGBoost       NaN             None      Zero Imputation   
 148   XGBoost       NaN             None      Zero Imputation   
 149   XGBoost       NaN             None      Zero Imputation   
 150   XGBoost       NaN             None      Zero Imputation   
 151   XGBoost       NaN             None      Zero Imputation   
 152   XGBoost       NaN             None      Zero Imputation   
 153   XGBoost       NaN             None      Zero Imputation   
 154   XGBoost       NaN            False      Zero Imputation   
 155   XGBoost       NaN             None      Zero Imputation   
 156   XGBoost       NaN             None      Zero Imputation   
 157   XGBoost       NaN             None      Zero Imputation   
 158   XGBoost       NaN             None      Zero Imputation   
 159   XGBoost       NaN             None      Zero Imputation   
 160   XGBoost       NaN             None      Zero Imputation   
 161   XGBoost       NaN             None      Zero Imputation   
 162   XGBoost       NaN             None      Zero Imputation   
 163   XGBoost       NaN             None      Zero Imputation   
 164   XGBoost       NaN             None      Zero Imputation   
 165   XGBoost       NaN             None      Zero Imputation   
 166   XGBoost       NaN             None      Zero Imputation   
 167   XGBoost       NaN             None      Zero Imputation   
 168   XGBoost       NaN             None      Zero Imputation   
 169   XGBoost       NaN              NaN      Zero Imputation   
 170   XGBoost       NaN             None      Zero Imputation   
 171   XGBoost       NaN             None      Zero Imputation   
 172   XGBoost       NaN             None      Zero Imputation   
 173   XGBoost       NaN             None      Zero Imputation   
 174   XGBoost       NaN             None      Zero Imputation   
 175   XGBoost       NaN             None      Zero Imputation   
 176   XGBoost       NaN             None      Zero Imputation   
 177   XGBoost       NaN             None      Zero Imputation   
 178   XGBoost       NaN             None      Zero Imputation   
 179   XGBoost       NaN             None      Zero Imputation   
 180   XGBoost       NaN             None      Zero Imputation   
 181   XGBoost       NaN             None      Zero Imputation   
 182   XGBoost       NaN             None      Zero Imputation   
 183   XGBoost       NaN             None      Zero Imputation   
 184   XGBoost  0.901475              NaN      Zero Imputation   
 185   XGBoost  0.165254              NaN      Zero Imputation   
 186   XGBoost  0.180556              NaN      Zero Imputation   
 187   XGBoost  0.172566              NaN      Zero Imputation   
 188   XGBoost  0.708571              NaN      Zero Imputation   
 189   XGBoost  0.338687              NaN      Zero Imputation   
 190   XGBoost  0.417141              NaN      Zero Imputation   
 191   XGBoost       NaN  binary:logistic      Zero Imputation   
 192   XGBoost       NaN             None      Zero Imputation   
 193   XGBoost       NaN             None      Zero Imputation   
 194   XGBoost       NaN             None      Zero Imputation   
 195   XGBoost       NaN             None      Zero Imputation   
 196   XGBoost       NaN             None      Zero Imputation   
 197   XGBoost       NaN             None      Zero Imputation   
 198   XGBoost       NaN             None      Zero Imputation   
 199   XGBoost       NaN             None      Zero Imputation   
 200   XGBoost       NaN            False      Zero Imputation   
 201   XGBoost       NaN             None      Zero Imputation   
 202   XGBoost       NaN             None      Zero Imputation   
 203   XGBoost       NaN             None      Zero Imputation   
 204   XGBoost       NaN             None      Zero Imputation   
 205   XGBoost       NaN             None      Zero Imputation   
 206   XGBoost       NaN             None      Zero Imputation   
 207   XGBoost       NaN             None      Zero Imputation   
 208   XGBoost       NaN             None      Zero Imputation   
 209   XGBoost       NaN             None      Zero Imputation   
 210   XGBoost       NaN             None      Zero Imputation   
 211   XGBoost       NaN             None      Zero Imputation   
 212   XGBoost       NaN             None      Zero Imputation   
 213   XGBoost       NaN             None      Zero Imputation   
 214   XGBoost       NaN             None      Zero Imputation   
 215   XGBoost       NaN              NaN      Zero Imputation   
 216   XGBoost       NaN             None      Zero Imputation   
 217   XGBoost       NaN             None      Zero Imputation   
 218   XGBoost       NaN             None      Zero Imputation   
 219   XGBoost       NaN             None      Zero Imputation   
 220   XGBoost       NaN             None      Zero Imputation   
 221   XGBoost       NaN             None      Zero Imputation   
 222   XGBoost       NaN             None      Zero Imputation   
 223   XGBoost       NaN             None      Zero Imputation   
 224   XGBoost       NaN             None      Zero Imputation   
 225   XGBoost       NaN             None      Zero Imputation   
 226   XGBoost       NaN             None      Zero Imputation   
 227   XGBoost       NaN             None      Zero Imputation   
 228   XGBoost       NaN             None      Zero Imputation   
 229   XGBoost       NaN             None      Zero Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 150  Hybrid Sampling (SMOTETomek)   
 151  Hybrid Sampling (SMOTETomek)   
 152  Hybrid Sampling (SMOTETomek)   
 153  Hybrid Sampling (SMOTETomek)   
 154  Hybrid Sampling (SMOTETomek)   
 155  Hybrid Sampling (SMOTETomek)   
 156  Hybrid Sampling (SMOTETomek)   
 157  Hybrid Sampling (SMOTETomek)   
 158  Hybrid Sampling (SMOTETomek)   
 159  Hybrid Sampling (SMOTETomek)   
 160  Hybrid Sampling (SMOTETomek)   
 161  Hybrid Sampling (SMOTETomek)   
 162  Hybrid Sampling (SMOTETomek)   
 163  Hybrid Sampling (SMOTETomek)   
 164  Hybrid Sampling (SMOTETomek)   
 165  Hybrid Sampling (SMOTETomek)   
 166  Hybrid Sampling (SMOTETomek)   
 167  Hybrid Sampling (SMOTETomek)   
 168  Hybrid Sampling (SMOTETomek)   
 169  Hybrid Sampling (SMOTETomek)   
 170  Hybrid Sampling (SMOTETomek)   
 171  Hybrid Sampling (SMOTETomek)   
 172  Hybrid Sampling (SMOTETomek)   
 173  Hybrid Sampling (SMOTETomek)   
 174  Hybrid Sampling (SMOTETomek)   
 175  Hybrid Sampling (SMOTETomek)   
 176  Hybrid Sampling (SMOTETomek)   
 177  Hybrid Sampling (SMOTETomek)   
 178  Hybrid Sampling (SMOTETomek)   
 179  Hybrid Sampling (SMOTETomek)   
 180  Hybrid Sampling (SMOTETomek)   
 181  Hybrid Sampling (SMOTETomek)   
 182  Hybrid Sampling (SMOTETomek)   
 183  Hybrid Sampling (SMOTETomek)   
 184  Hybrid Sampling (SMOTETomek)   
 185  Hybrid Sampling (SMOTETomek)   
 186  Hybrid Sampling (SMOTETomek)   
 187  Hybrid Sampling (SMOTETomek)   
 188  Hybrid Sampling (SMOTETomek)   
 189  Hybrid Sampling (SMOTETomek)   
 190  Hybrid Sampling (SMOTETomek)   
 191  Hybrid Sampling (SMOTETomek)   
 192  Hybrid Sampling (SMOTETomek)   
 193  Hybrid Sampling (SMOTETomek)   
 194  Hybrid Sampling (SMOTETomek)   
 195  Hybrid Sampling (SMOTETomek)   
 196  Hybrid Sampling (SMOTETomek)   
 197  Hybrid Sampling (SMOTETomek)   
 198  Hybrid Sampling (SMOTETomek)   
 199  Hybrid Sampling (SMOTETomek)   
 200  Hybrid Sampling (SMOTETomek)   
 201  Hybrid Sampling (SMOTETomek)   
 202  Hybrid Sampling (SMOTETomek)   
 203  Hybrid Sampling (SMOTETomek)   
 204  Hybrid Sampling (SMOTETomek)   
 205  Hybrid Sampling (SMOTETomek)   
 206  Hybrid Sampling (SMOTETomek)   
 207  Hybrid Sampling (SMOTETomek)   
 208  Hybrid Sampling (SMOTETomek)   
 209  Hybrid Sampling (SMOTETomek)   
 210  Hybrid Sampling (SMOTETomek)   
 211  Hybrid Sampling (SMOTETomek)   
 212  Hybrid Sampling (SMOTETomek)   
 213  Hybrid Sampling (SMOTETomek)   
 214  Hybrid Sampling (SMOTETomek)   
 215  Hybrid Sampling (SMOTETomek)   
 216  Hybrid Sampling (SMOTETomek)   
 217  Hybrid Sampling (SMOTETomek)   
 218  Hybrid Sampling (SMOTETomek)   
 219  Hybrid Sampling (SMOTETomek)   
 220  Hybrid Sampling (SMOTETomek)   
 221  Hybrid Sampling (SMOTETomek)   
 222  Hybrid Sampling (SMOTETomek)   
 223  Hybrid Sampling (SMOTETomek)   
 224  Hybrid Sampling (SMOTETomek)   
 225  Hybrid Sampling (SMOTETomek)   
 226  Hybrid Sampling (SMOTETomek)   
 227  Hybrid Sampling (SMOTETomek)   
 228  Hybrid Sampling (SMOTETomek)   
 229  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 1    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 2    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 3    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 4    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 5    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 6    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 7    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 8    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 9    XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 10   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 11   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 12   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 13   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 14   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 15   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 16   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 17   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 18   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 19   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 20   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 21   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 22   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 23   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 24   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 25   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 26   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 27   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 28   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 29   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 30   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 31   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 32   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 33   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 34   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 35   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 36   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 37   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 38   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 39   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 40   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 41   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 42   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 43   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 44   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 45   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 46   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 47   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 48   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 49   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 50   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 51   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 52   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 53   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 54   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 55   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 56   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 57   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 58   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 59   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 60   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 61   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 62   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 63   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 64   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 65   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 66   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 67   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 68   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 69   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 70   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 71   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 72   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 73   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 74   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 75   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 76   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 77   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 78   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 79   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 80   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 81   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 82   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 83   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 84   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 85   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 86   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 87   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 88   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 89   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 90   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 91   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 92   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 93   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 94   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 95   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 96   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 97   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 98   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 99   XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 100  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 101  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 102  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 103  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 104  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 105  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 106  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 107  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 108  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 109  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 110  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 111  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 112  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 113  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 114  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 115  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 116  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 117  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 118  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 119  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 120  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 121  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 122  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 123  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 124  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 125  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 126  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 127  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 128  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 129  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 130  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 131  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 132  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 133  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 134  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 135  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 136  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 137  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 138  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 139  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 140  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 141  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 142  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 143  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 144  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 145  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 146  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 147  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 148  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 149  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 150  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 151  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 152  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 153  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 154  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 155  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 156  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 157  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 158  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 159  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 160  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 161  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 162  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 163  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 164  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 165  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 166  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 167  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 168  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 169  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 170  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 171  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 172  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 173  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 174  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 175  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 176  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 177  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 178  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 179  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 180  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 181  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 182  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 183  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 184  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 185  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 186  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 187  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 188  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 189  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 190  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 191  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 192  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 193  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 194  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 195  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 196  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 197  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 198  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 199  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 200  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 201  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 202  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 203  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 204  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 205  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 206  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 207  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 208  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 209  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 210  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 211  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 212  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 213  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 214  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 215  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 216  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 217  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 218  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 219  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 220  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 221  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 222  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 223  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 224  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 225  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 226  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 227  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 228  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  
 229  XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.888333              NaN      Mean Imputation   
 1     XGBoost  0.154982              NaN      Mean Imputation   
 2     XGBoost  0.177215              NaN      Mean Imputation   
 3     XGBoost  0.165354              NaN      Mean Imputation   
 4     XGBoost  0.659546              NaN      Mean Imputation   
 5     XGBoost  0.239434              NaN      Mean Imputation   
 6     XGBoost  0.319093              NaN      Mean Imputation   
 7     XGBoost       NaN  binary:logistic      Mean Imputation   
 8     XGBoost       NaN             None      Mean Imputation   
 9     XGBoost       NaN             None      Mean Imputation   
 10    XGBoost       NaN             None      Mean Imputation   
 11    XGBoost       NaN             None      Mean Imputation   
 12    XGBoost       NaN             None      Mean Imputation   
 13    XGBoost       NaN             None      Mean Imputation   
 14    XGBoost       NaN             None      Mean Imputation   
 15    XGBoost       NaN             None      Mean Imputation   
 16    XGBoost       NaN            False      Mean Imputation   
 17    XGBoost       NaN             None      Mean Imputation   
 18    XGBoost       NaN             None      Mean Imputation   
 19    XGBoost       NaN             None      Mean Imputation   
 20    XGBoost       NaN             None      Mean Imputation   
 21    XGBoost       NaN             None      Mean Imputation   
 22    XGBoost       NaN             None      Mean Imputation   
 23    XGBoost       NaN             None      Mean Imputation   
 24    XGBoost       NaN             None      Mean Imputation   
 25    XGBoost       NaN             None      Mean Imputation   
 26    XGBoost       NaN             None      Mean Imputation   
 27    XGBoost       NaN             None      Mean Imputation   
 28    XGBoost       NaN             None      Mean Imputation   
 29    XGBoost       NaN             None      Mean Imputation   
 30    XGBoost       NaN             None      Mean Imputation   
 31    XGBoost       NaN              NaN      Mean Imputation   
 32    XGBoost       NaN             None      Mean Imputation   
 33    XGBoost       NaN             None      Mean Imputation   
 34    XGBoost       NaN             None      Mean Imputation   
 35    XGBoost       NaN             None      Mean Imputation   
 36    XGBoost       NaN             None      Mean Imputation   
 37    XGBoost       NaN             None      Mean Imputation   
 38    XGBoost       NaN             None      Mean Imputation   
 39    XGBoost       NaN             None      Mean Imputation   
 40    XGBoost       NaN             None      Mean Imputation   
 41    XGBoost       NaN             None      Mean Imputation   
 42    XGBoost       NaN             None      Mean Imputation   
 43    XGBoost       NaN             None      Mean Imputation   
 44    XGBoost       NaN             None      Mean Imputation   
 45    XGBoost       NaN             None      Mean Imputation   
 46    XGBoost  0.907032              NaN      Mean Imputation   
 47    XGBoost  0.107884              NaN      Mean Imputation   
 48    XGBoost  0.158537              NaN      Mean Imputation   
 49    XGBoost  0.128395              NaN      Mean Imputation   
 50    XGBoost  0.698087              NaN      Mean Imputation   
 51    XGBoost  0.299992              NaN      Mean Imputation   
 52    XGBoost  0.396174              NaN      Mean Imputation   
 53    XGBoost       NaN  binary:logistic      Mean Imputation   
 54    XGBoost       NaN             None      Mean Imputation   
 55    XGBoost       NaN             None      Mean Imputation   
 56    XGBoost       NaN             None      Mean Imputation   
 57    XGBoost       NaN             None      Mean Imputation   
 58    XGBoost       NaN             None      Mean Imputation   
 59    XGBoost       NaN             None      Mean Imputation   
 60    XGBoost       NaN             None      Mean Imputation   
 61    XGBoost       NaN             None      Mean Imputation   
 62    XGBoost       NaN            False      Mean Imputation   
 63    XGBoost       NaN             None      Mean Imputation   
 64    XGBoost       NaN             None      Mean Imputation   
 65    XGBoost       NaN             None      Mean Imputation   
 66    XGBoost       NaN             None      Mean Imputation   
 67    XGBoost       NaN             None      Mean Imputation   
 68    XGBoost       NaN             None      Mean Imputation   
 69    XGBoost       NaN             None      Mean Imputation   
 70    XGBoost       NaN             None      Mean Imputation   
 71    XGBoost       NaN             None      Mean Imputation   
 72    XGBoost       NaN             None      Mean Imputation   
 73    XGBoost       NaN             None      Mean Imputation   
 74    XGBoost       NaN             None      Mean Imputation   
 75    XGBoost       NaN             None      Mean Imputation   
 76    XGBoost       NaN             None      Mean Imputation   
 77    XGBoost       NaN              NaN      Mean Imputation   
 78    XGBoost       NaN             None      Mean Imputation   
 79    XGBoost       NaN             None      Mean Imputation   
 80    XGBoost       NaN             None      Mean Imputation   
 81    XGBoost       NaN             None      Mean Imputation   
 82    XGBoost       NaN             None      Mean Imputation   
 83    XGBoost       NaN             None      Mean Imputation   
 84    XGBoost       NaN             None      Mean Imputation   
 85    XGBoost       NaN             None      Mean Imputation   
 86    XGBoost       NaN             None      Mean Imputation   
 87    XGBoost       NaN             None      Mean Imputation   
 88    XGBoost       NaN             None      Mean Imputation   
 89    XGBoost       NaN             None      Mean Imputation   
 90    XGBoost       NaN             None      Mean Imputation   
 91    XGBoost       NaN             None      Mean Imputation   
 92    XGBoost  0.898604              NaN      Mean Imputation   
 93    XGBoost  0.143885              NaN      Mean Imputation   
 94    XGBoost  0.213904              NaN      Mean Imputation   
 95    XGBoost  0.172043              NaN      Mean Imputation   
 96    XGBoost  0.669749              NaN      Mean Imputation   
 97    XGBoost  0.278310              NaN      Mean Imputation   
 98    XGBoost  0.339498              NaN      Mean Imputation   
 99    XGBoost       NaN  binary:logistic      Mean Imputation   
 100   XGBoost       NaN             None      Mean Imputation   
 101   XGBoost       NaN             None      Mean Imputation   
 102   XGBoost       NaN             None      Mean Imputation   
 103   XGBoost       NaN             None      Mean Imputation   
 104   XGBoost       NaN             None      Mean Imputation   
 105   XGBoost       NaN             None      Mean Imputation   
 106   XGBoost       NaN             None      Mean Imputation   
 107   XGBoost       NaN             None      Mean Imputation   
 108   XGBoost       NaN            False      Mean Imputation   
 109   XGBoost       NaN             None      Mean Imputation   
 110   XGBoost       NaN             None      Mean Imputation   
 111   XGBoost       NaN             None      Mean Imputation   
 112   XGBoost       NaN             None      Mean Imputation   
 113   XGBoost       NaN             None      Mean Imputation   
 114   XGBoost       NaN             None      Mean Imputation   
 115   XGBoost       NaN             None      Mean Imputation   
 116   XGBoost       NaN             None      Mean Imputation   
 117   XGBoost       NaN             None      Mean Imputation   
 118   XGBoost       NaN             None      Mean Imputation   
 119   XGBoost       NaN             None      Mean Imputation   
 120   XGBoost       NaN             None      Mean Imputation   
 121   XGBoost       NaN             None      Mean Imputation   
 122   XGBoost       NaN             None      Mean Imputation   
 123   XGBoost       NaN              NaN      Mean Imputation   
 124   XGBoost       NaN             None      Mean Imputation   
 125   XGBoost       NaN             None      Mean Imputation   
 126   XGBoost       NaN             None      Mean Imputation   
 127   XGBoost       NaN             None      Mean Imputation   
 128   XGBoost       NaN             None      Mean Imputation   
 129   XGBoost       NaN             None      Mean Imputation   
 130   XGBoost       NaN             None      Mean Imputation   
 131   XGBoost       NaN             None      Mean Imputation   
 132   XGBoost       NaN             None      Mean Imputation   
 133   XGBoost       NaN             None      Mean Imputation   
 134   XGBoost       NaN             None      Mean Imputation   
 135   XGBoost       NaN             None      Mean Imputation   
 136   XGBoost       NaN             None      Mean Imputation   
 137   XGBoost       NaN             None      Mean Imputation   
 138   XGBoost  0.893836              NaN      Mean Imputation   
 139   XGBoost  0.126354              NaN      Mean Imputation   
 140   XGBoost  0.178571              NaN      Mean Imputation   
 141   XGBoost  0.147992              NaN      Mean Imputation   
 142   XGBoost  0.668641              NaN      Mean Imputation   
 143   XGBoost  0.259717              NaN      Mean Imputation   
 144   XGBoost  0.337282              NaN      Mean Imputation   
 145   XGBoost       NaN  binary:logistic      Mean Imputation   
 146   XGBoost       NaN             None      Mean Imputation   
 147   XGBoost       NaN             None      Mean Imputation   
 148   XGBoost       NaN             None      Mean Imputation   
 149   XGBoost       NaN             None      Mean Imputation   
 150   XGBoost       NaN             None      Mean Imputation   
 151   XGBoost       NaN             None      Mean Imputation   
 152   XGBoost       NaN             None      Mean Imputation   
 153   XGBoost       NaN             None      Mean Imputation   
 154   XGBoost       NaN            False      Mean Imputation   
 155   XGBoost       NaN             None      Mean Imputation   
 156   XGBoost       NaN             None      Mean Imputation   
 157   XGBoost       NaN             None      Mean Imputation   
 158   XGBoost       NaN             None      Mean Imputation   
 159   XGBoost       NaN             None      Mean Imputation   
 160   XGBoost       NaN             None      Mean Imputation   
 161   XGBoost       NaN             None      Mean Imputation   
 162   XGBoost       NaN             None      Mean Imputation   
 163   XGBoost       NaN             None      Mean Imputation   
 164   XGBoost       NaN             None      Mean Imputation   
 165   XGBoost       NaN             None      Mean Imputation   
 166   XGBoost       NaN             None      Mean Imputation   
 167   XGBoost       NaN             None      Mean Imputation   
 168   XGBoost       NaN             None      Mean Imputation   
 169   XGBoost       NaN              NaN      Mean Imputation   
 170   XGBoost       NaN             None      Mean Imputation   
 171   XGBoost       NaN             None      Mean Imputation   
 172   XGBoost       NaN             None      Mean Imputation   
 173   XGBoost       NaN             None      Mean Imputation   
 174   XGBoost       NaN             None      Mean Imputation   
 175   XGBoost       NaN             None      Mean Imputation   
 176   XGBoost       NaN             None      Mean Imputation   
 177   XGBoost       NaN             None      Mean Imputation   
 178   XGBoost       NaN             None      Mean Imputation   
 179   XGBoost       NaN             None      Mean Imputation   
 180   XGBoost       NaN             None      Mean Imputation   
 181   XGBoost       NaN             None      Mean Imputation   
 182   XGBoost       NaN             None      Mean Imputation   
 183   XGBoost       NaN             None      Mean Imputation   
 184   XGBoost  0.889884              NaN      Mean Imputation   
 185   XGBoost  0.144366              NaN      Mean Imputation   
 186   XGBoost  0.189815              NaN      Mean Imputation   
 187   XGBoost  0.164000              NaN      Mean Imputation   
 188   XGBoost  0.702099              NaN      Mean Imputation   
 189   XGBoost  0.349193              NaN      Mean Imputation   
 190   XGBoost  0.404199              NaN      Mean Imputation   
 191   XGBoost       NaN  binary:logistic      Mean Imputation   
 192   XGBoost       NaN             None      Mean Imputation   
 193   XGBoost       NaN             None      Mean Imputation   
 194   XGBoost       NaN             None      Mean Imputation   
 195   XGBoost       NaN             None      Mean Imputation   
 196   XGBoost       NaN             None      Mean Imputation   
 197   XGBoost       NaN             None      Mean Imputation   
 198   XGBoost       NaN             None      Mean Imputation   
 199   XGBoost       NaN             None      Mean Imputation   
 200   XGBoost       NaN            False      Mean Imputation   
 201   XGBoost       NaN             None      Mean Imputation   
 202   XGBoost       NaN             None      Mean Imputation   
 203   XGBoost       NaN             None      Mean Imputation   
 204   XGBoost       NaN             None      Mean Imputation   
 205   XGBoost       NaN             None      Mean Imputation   
 206   XGBoost       NaN             None      Mean Imputation   
 207   XGBoost       NaN             None      Mean Imputation   
 208   XGBoost       NaN             None      Mean Imputation   
 209   XGBoost       NaN             None      Mean Imputation   
 210   XGBoost       NaN             None      Mean Imputation   
 211   XGBoost       NaN             None      Mean Imputation   
 212   XGBoost       NaN             None      Mean Imputation   
 213   XGBoost       NaN             None      Mean Imputation   
 214   XGBoost       NaN             None      Mean Imputation   
 215   XGBoost       NaN              NaN      Mean Imputation   
 216   XGBoost       NaN             None      Mean Imputation   
 217   XGBoost       NaN             None      Mean Imputation   
 218   XGBoost       NaN             None      Mean Imputation   
 219   XGBoost       NaN             None      Mean Imputation   
 220   XGBoost       NaN             None      Mean Imputation   
 221   XGBoost       NaN             None      Mean Imputation   
 222   XGBoost       NaN             None      Mean Imputation   
 223   XGBoost       NaN             None      Mean Imputation   
 224   XGBoost       NaN             None      Mean Imputation   
 225   XGBoost       NaN             None      Mean Imputation   
 226   XGBoost       NaN             None      Mean Imputation   
 227   XGBoost       NaN             None      Mean Imputation   
 228   XGBoost       NaN             None      Mean Imputation   
 229   XGBoost       NaN             None      Mean Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 150  Hybrid Sampling (SMOTETomek)   
 151  Hybrid Sampling (SMOTETomek)   
 152  Hybrid Sampling (SMOTETomek)   
 153  Hybrid Sampling (SMOTETomek)   
 154  Hybrid Sampling (SMOTETomek)   
 155  Hybrid Sampling (SMOTETomek)   
 156  Hybrid Sampling (SMOTETomek)   
 157  Hybrid Sampling (SMOTETomek)   
 158  Hybrid Sampling (SMOTETomek)   
 159  Hybrid Sampling (SMOTETomek)   
 160  Hybrid Sampling (SMOTETomek)   
 161  Hybrid Sampling (SMOTETomek)   
 162  Hybrid Sampling (SMOTETomek)   
 163  Hybrid Sampling (SMOTETomek)   
 164  Hybrid Sampling (SMOTETomek)   
 165  Hybrid Sampling (SMOTETomek)   
 166  Hybrid Sampling (SMOTETomek)   
 167  Hybrid Sampling (SMOTETomek)   
 168  Hybrid Sampling (SMOTETomek)   
 169  Hybrid Sampling (SMOTETomek)   
 170  Hybrid Sampling (SMOTETomek)   
 171  Hybrid Sampling (SMOTETomek)   
 172  Hybrid Sampling (SMOTETomek)   
 173  Hybrid Sampling (SMOTETomek)   
 174  Hybrid Sampling (SMOTETomek)   
 175  Hybrid Sampling (SMOTETomek)   
 176  Hybrid Sampling (SMOTETomek)   
 177  Hybrid Sampling (SMOTETomek)   
 178  Hybrid Sampling (SMOTETomek)   
 179  Hybrid Sampling (SMOTETomek)   
 180  Hybrid Sampling (SMOTETomek)   
 181  Hybrid Sampling (SMOTETomek)   
 182  Hybrid Sampling (SMOTETomek)   
 183  Hybrid Sampling (SMOTETomek)   
 184  Hybrid Sampling (SMOTETomek)   
 185  Hybrid Sampling (SMOTETomek)   
 186  Hybrid Sampling (SMOTETomek)   
 187  Hybrid Sampling (SMOTETomek)   
 188  Hybrid Sampling (SMOTETomek)   
 189  Hybrid Sampling (SMOTETomek)   
 190  Hybrid Sampling (SMOTETomek)   
 191  Hybrid Sampling (SMOTETomek)   
 192  Hybrid Sampling (SMOTETomek)   
 193  Hybrid Sampling (SMOTETomek)   
 194  Hybrid Sampling (SMOTETomek)   
 195  Hybrid Sampling (SMOTETomek)   
 196  Hybrid Sampling (SMOTETomek)   
 197  Hybrid Sampling (SMOTETomek)   
 198  Hybrid Sampling (SMOTETomek)   
 199  Hybrid Sampling (SMOTETomek)   
 200  Hybrid Sampling (SMOTETomek)   
 201  Hybrid Sampling (SMOTETomek)   
 202  Hybrid Sampling (SMOTETomek)   
 203  Hybrid Sampling (SMOTETomek)   
 204  Hybrid Sampling (SMOTETomek)   
 205  Hybrid Sampling (SMOTETomek)   
 206  Hybrid Sampling (SMOTETomek)   
 207  Hybrid Sampling (SMOTETomek)   
 208  Hybrid Sampling (SMOTETomek)   
 209  Hybrid Sampling (SMOTETomek)   
 210  Hybrid Sampling (SMOTETomek)   
 211  Hybrid Sampling (SMOTETomek)   
 212  Hybrid Sampling (SMOTETomek)   
 213  Hybrid Sampling (SMOTETomek)   
 214  Hybrid Sampling (SMOTETomek)   
 215  Hybrid Sampling (SMOTETomek)   
 216  Hybrid Sampling (SMOTETomek)   
 217  Hybrid Sampling (SMOTETomek)   
 218  Hybrid Sampling (SMOTETomek)   
 219  Hybrid Sampling (SMOTETomek)   
 220  Hybrid Sampling (SMOTETomek)   
 221  Hybrid Sampling (SMOTETomek)   
 222  Hybrid Sampling (SMOTETomek)   
 223  Hybrid Sampling (SMOTETomek)   
 224  Hybrid Sampling (SMOTETomek)   
 225  Hybrid Sampling (SMOTETomek)   
 226  Hybrid Sampling (SMOTETomek)   
 227  Hybrid Sampling (SMOTETomek)   
 228  Hybrid Sampling (SMOTETomek)   
 229  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 1    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 2    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 3    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 4    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 5    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 6    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 7    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 8    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 9    XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 10   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 11   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 12   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 13   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 14   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 15   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 16   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 17   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 18   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 19   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 20   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 21   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 22   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 23   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 24   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 25   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 26   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 27   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 28   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 29   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 30   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 31   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 32   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 33   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 34   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 35   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 36   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 37   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 38   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 39   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 40   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 41   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 42   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 43   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 44   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 45   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 46   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 47   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 48   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 49   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 50   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 51   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 52   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 53   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 54   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 55   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 56   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 57   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 58   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 59   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 60   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 61   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 62   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 63   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 64   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 65   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 66   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 67   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 68   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 69   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 70   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 71   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 72   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 73   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 74   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 75   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 76   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 77   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 78   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 79   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 80   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 81   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 82   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 83   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 84   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 85   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 86   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 87   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 88   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 89   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 90   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 91   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 92   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 93   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 94   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 95   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 96   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 97   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 98   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 99   XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 100  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 101  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 102  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 103  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 104  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 105  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 106  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 107  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 108  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 109  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 110  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 111  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 112  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 113  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 114  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 115  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 116  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 117  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 118  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 119  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 120  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 121  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 122  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 123  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 124  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 125  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 126  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 127  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 128  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 129  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 130  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 131  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 132  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 133  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 134  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 135  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 136  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 137  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 138  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 139  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 140  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 141  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 142  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 143  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 144  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 145  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 146  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 147  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 148  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 149  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 150  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 151  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 152  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 153  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 154  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 155  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 156  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 157  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 158  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 159  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 160  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 161  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 162  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 163  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 164  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 165  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 166  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 167  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 168  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 169  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 170  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 171  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 172  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 173  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 174  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 175  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 176  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 177  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 178  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 179  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 180  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 181  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 182  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 183  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 184  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 185  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 186  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 187  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 188  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 189  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 190  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 191  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 192  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 193  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 194  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 195  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 196  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 197  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 198  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 199  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 200  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 201  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 202  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 203  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 204  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 205  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 206  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 207  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 208  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 209  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 210  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 211  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 212  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 213  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 214  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 215  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 216  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 217  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 218  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 219  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 220  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 221  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 222  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 223  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 224  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 225  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 226  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 227  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 228  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  
 229  XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...  ,
     Algorithm   Metrics  Hyperparameters Imputation Technique  \
 0     XGBoost  0.887806              NaN    Median Imputation   
 1     XGBoost  0.132296              NaN    Median Imputation   
 2     XGBoost  0.143460              NaN    Median Imputation   
 3     XGBoost  0.137652              NaN    Median Imputation   
 4     XGBoost  0.653358              NaN    Median Imputation   
 5     XGBoost  0.244218              NaN    Median Imputation   
 6     XGBoost  0.306715              NaN    Median Imputation   
 7     XGBoost       NaN  binary:logistic    Median Imputation   
 8     XGBoost       NaN             None    Median Imputation   
 9     XGBoost       NaN             None    Median Imputation   
 10    XGBoost       NaN             None    Median Imputation   
 11    XGBoost       NaN             None    Median Imputation   
 12    XGBoost       NaN             None    Median Imputation   
 13    XGBoost       NaN             None    Median Imputation   
 14    XGBoost       NaN             None    Median Imputation   
 15    XGBoost       NaN             None    Median Imputation   
 16    XGBoost       NaN            False    Median Imputation   
 17    XGBoost       NaN             None    Median Imputation   
 18    XGBoost       NaN             None    Median Imputation   
 19    XGBoost       NaN             None    Median Imputation   
 20    XGBoost       NaN             None    Median Imputation   
 21    XGBoost       NaN             None    Median Imputation   
 22    XGBoost       NaN             None    Median Imputation   
 23    XGBoost       NaN             None    Median Imputation   
 24    XGBoost       NaN             None    Median Imputation   
 25    XGBoost       NaN             None    Median Imputation   
 26    XGBoost       NaN             None    Median Imputation   
 27    XGBoost       NaN             None    Median Imputation   
 28    XGBoost       NaN             None    Median Imputation   
 29    XGBoost       NaN             None    Median Imputation   
 30    XGBoost       NaN             None    Median Imputation   
 31    XGBoost       NaN              NaN    Median Imputation   
 32    XGBoost       NaN             None    Median Imputation   
 33    XGBoost       NaN             None    Median Imputation   
 34    XGBoost       NaN             None    Median Imputation   
 35    XGBoost       NaN             None    Median Imputation   
 36    XGBoost       NaN             None    Median Imputation   
 37    XGBoost       NaN             None    Median Imputation   
 38    XGBoost       NaN             None    Median Imputation   
 39    XGBoost       NaN             None    Median Imputation   
 40    XGBoost       NaN             None    Median Imputation   
 41    XGBoost       NaN             None    Median Imputation   
 42    XGBoost       NaN             None    Median Imputation   
 43    XGBoost       NaN             None    Median Imputation   
 44    XGBoost       NaN             None    Median Imputation   
 45    XGBoost       NaN             None    Median Imputation   
 46    XGBoost  0.905452              NaN    Median Imputation   
 47    XGBoost  0.123552              NaN    Median Imputation   
 48    XGBoost  0.195122              NaN    Median Imputation   
 49    XGBoost  0.151300              NaN    Median Imputation   
 50    XGBoost  0.679774              NaN    Median Imputation   
 51    XGBoost  0.284064              NaN    Median Imputation   
 52    XGBoost  0.359548              NaN    Median Imputation   
 53    XGBoost       NaN  binary:logistic    Median Imputation   
 54    XGBoost       NaN             None    Median Imputation   
 55    XGBoost       NaN             None    Median Imputation   
 56    XGBoost       NaN             None    Median Imputation   
 57    XGBoost       NaN             None    Median Imputation   
 58    XGBoost       NaN             None    Median Imputation   
 59    XGBoost       NaN             None    Median Imputation   
 60    XGBoost       NaN             None    Median Imputation   
 61    XGBoost       NaN             None    Median Imputation   
 62    XGBoost       NaN            False    Median Imputation   
 63    XGBoost       NaN             None    Median Imputation   
 64    XGBoost       NaN             None    Median Imputation   
 65    XGBoost       NaN             None    Median Imputation   
 66    XGBoost       NaN             None    Median Imputation   
 67    XGBoost       NaN             None    Median Imputation   
 68    XGBoost       NaN             None    Median Imputation   
 69    XGBoost       NaN             None    Median Imputation   
 70    XGBoost       NaN             None    Median Imputation   
 71    XGBoost       NaN             None    Median Imputation   
 72    XGBoost       NaN             None    Median Imputation   
 73    XGBoost       NaN             None    Median Imputation   
 74    XGBoost       NaN             None    Median Imputation   
 75    XGBoost       NaN             None    Median Imputation   
 76    XGBoost       NaN             None    Median Imputation   
 77    XGBoost       NaN              NaN    Median Imputation   
 78    XGBoost       NaN             None    Median Imputation   
 79    XGBoost       NaN             None    Median Imputation   
 80    XGBoost       NaN             None    Median Imputation   
 81    XGBoost       NaN             None    Median Imputation   
 82    XGBoost       NaN             None    Median Imputation   
 83    XGBoost       NaN             None    Median Imputation   
 84    XGBoost       NaN             None    Median Imputation   
 85    XGBoost       NaN             None    Median Imputation   
 86    XGBoost       NaN             None    Median Imputation   
 87    XGBoost       NaN             None    Median Imputation   
 88    XGBoost       NaN             None    Median Imputation   
 89    XGBoost       NaN             None    Median Imputation   
 90    XGBoost       NaN             None    Median Imputation   
 91    XGBoost       NaN             None    Median Imputation   
 92    XGBoost  0.895180              NaN    Median Imputation   
 93    XGBoost  0.116364              NaN    Median Imputation   
 94    XGBoost  0.171123              NaN    Median Imputation   
 95    XGBoost  0.138528              NaN    Median Imputation   
 96    XGBoost  0.668646              NaN    Median Imputation   
 97    XGBoost  0.266230              NaN    Median Imputation   
 98    XGBoost  0.337292              NaN    Median Imputation   
 99    XGBoost       NaN  binary:logistic    Median Imputation   
 100   XGBoost       NaN             None    Median Imputation   
 101   XGBoost       NaN             None    Median Imputation   
 102   XGBoost       NaN             None    Median Imputation   
 103   XGBoost       NaN             None    Median Imputation   
 104   XGBoost       NaN             None    Median Imputation   
 105   XGBoost       NaN             None    Median Imputation   
 106   XGBoost       NaN             None    Median Imputation   
 107   XGBoost       NaN             None    Median Imputation   
 108   XGBoost       NaN            False    Median Imputation   
 109   XGBoost       NaN             None    Median Imputation   
 110   XGBoost       NaN             None    Median Imputation   
 111   XGBoost       NaN             None    Median Imputation   
 112   XGBoost       NaN             None    Median Imputation   
 113   XGBoost       NaN             None    Median Imputation   
 114   XGBoost       NaN             None    Median Imputation   
 115   XGBoost       NaN             None    Median Imputation   
 116   XGBoost       NaN             None    Median Imputation   
 117   XGBoost       NaN             None    Median Imputation   
 118   XGBoost       NaN             None    Median Imputation   
 119   XGBoost       NaN             None    Median Imputation   
 120   XGBoost       NaN             None    Median Imputation   
 121   XGBoost       NaN             None    Median Imputation   
 122   XGBoost       NaN             None    Median Imputation   
 123   XGBoost       NaN              NaN    Median Imputation   
 124   XGBoost       NaN             None    Median Imputation   
 125   XGBoost       NaN             None    Median Imputation   
 126   XGBoost       NaN             None    Median Imputation   
 127   XGBoost       NaN             None    Median Imputation   
 128   XGBoost       NaN             None    Median Imputation   
 129   XGBoost       NaN             None    Median Imputation   
 130   XGBoost       NaN             None    Median Imputation   
 131   XGBoost       NaN             None    Median Imputation   
 132   XGBoost       NaN             None    Median Imputation   
 133   XGBoost       NaN             None    Median Imputation   
 134   XGBoost       NaN             None    Median Imputation   
 135   XGBoost       NaN             None    Median Imputation   
 136   XGBoost       NaN             None    Median Imputation   
 137   XGBoost       NaN             None    Median Imputation   
 138   XGBoost  0.894099              NaN    Median Imputation   
 139   XGBoost  0.132143              NaN    Median Imputation   
 140   XGBoost  0.188776              NaN    Median Imputation   
 141   XGBoost  0.155462              NaN    Median Imputation   
 142   XGBoost  0.680699              NaN    Median Imputation   
 143   XGBoost  0.296287              NaN    Median Imputation   
 144   XGBoost  0.361399              NaN    Median Imputation   
 145   XGBoost       NaN  binary:logistic    Median Imputation   
 146   XGBoost       NaN             None    Median Imputation   
 147   XGBoost       NaN             None    Median Imputation   
 148   XGBoost       NaN             None    Median Imputation   
 149   XGBoost       NaN             None    Median Imputation   
 150   XGBoost       NaN             None    Median Imputation   
 151   XGBoost       NaN             None    Median Imputation   
 152   XGBoost       NaN             None    Median Imputation   
 153   XGBoost       NaN             None    Median Imputation   
 154   XGBoost       NaN            False    Median Imputation   
 155   XGBoost       NaN             None    Median Imputation   
 156   XGBoost       NaN             None    Median Imputation   
 157   XGBoost       NaN             None    Median Imputation   
 158   XGBoost       NaN             None    Median Imputation   
 159   XGBoost       NaN             None    Median Imputation   
 160   XGBoost       NaN             None    Median Imputation   
 161   XGBoost       NaN             None    Median Imputation   
 162   XGBoost       NaN             None    Median Imputation   
 163   XGBoost       NaN             None    Median Imputation   
 164   XGBoost       NaN             None    Median Imputation   
 165   XGBoost       NaN             None    Median Imputation   
 166   XGBoost       NaN             None    Median Imputation   
 167   XGBoost       NaN             None    Median Imputation   
 168   XGBoost       NaN             None    Median Imputation   
 169   XGBoost       NaN              NaN    Median Imputation   
 170   XGBoost       NaN             None    Median Imputation   
 171   XGBoost       NaN             None    Median Imputation   
 172   XGBoost       NaN             None    Median Imputation   
 173   XGBoost       NaN             None    Median Imputation   
 174   XGBoost       NaN             None    Median Imputation   
 175   XGBoost       NaN             None    Median Imputation   
 176   XGBoost       NaN             None    Median Imputation   
 177   XGBoost       NaN             None    Median Imputation   
 178   XGBoost       NaN             None    Median Imputation   
 179   XGBoost       NaN             None    Median Imputation   
 180   XGBoost       NaN             None    Median Imputation   
 181   XGBoost       NaN             None    Median Imputation   
 182   XGBoost       NaN             None    Median Imputation   
 183   XGBoost       NaN             None    Median Imputation   
 184   XGBoost  0.894362              NaN    Median Imputation   
 185   XGBoost  0.150943              NaN    Median Imputation   
 186   XGBoost  0.185185              NaN    Median Imputation   
 187   XGBoost  0.166320              NaN    Median Imputation   
 188   XGBoost  0.695182              NaN    Median Imputation   
 189   XGBoost  0.309202              NaN    Median Imputation   
 190   XGBoost  0.390364              NaN    Median Imputation   
 191   XGBoost       NaN  binary:logistic    Median Imputation   
 192   XGBoost       NaN             None    Median Imputation   
 193   XGBoost       NaN             None    Median Imputation   
 194   XGBoost       NaN             None    Median Imputation   
 195   XGBoost       NaN             None    Median Imputation   
 196   XGBoost       NaN             None    Median Imputation   
 197   XGBoost       NaN             None    Median Imputation   
 198   XGBoost       NaN             None    Median Imputation   
 199   XGBoost       NaN             None    Median Imputation   
 200   XGBoost       NaN            False    Median Imputation   
 201   XGBoost       NaN             None    Median Imputation   
 202   XGBoost       NaN             None    Median Imputation   
 203   XGBoost       NaN             None    Median Imputation   
 204   XGBoost       NaN             None    Median Imputation   
 205   XGBoost       NaN             None    Median Imputation   
 206   XGBoost       NaN             None    Median Imputation   
 207   XGBoost       NaN             None    Median Imputation   
 208   XGBoost       NaN             None    Median Imputation   
 209   XGBoost       NaN             None    Median Imputation   
 210   XGBoost       NaN             None    Median Imputation   
 211   XGBoost       NaN             None    Median Imputation   
 212   XGBoost       NaN             None    Median Imputation   
 213   XGBoost       NaN             None    Median Imputation   
 214   XGBoost       NaN             None    Median Imputation   
 215   XGBoost       NaN              NaN    Median Imputation   
 216   XGBoost       NaN             None    Median Imputation   
 217   XGBoost       NaN             None    Median Imputation   
 218   XGBoost       NaN             None    Median Imputation   
 219   XGBoost       NaN             None    Median Imputation   
 220   XGBoost       NaN             None    Median Imputation   
 221   XGBoost       NaN             None    Median Imputation   
 222   XGBoost       NaN             None    Median Imputation   
 223   XGBoost       NaN             None    Median Imputation   
 224   XGBoost       NaN             None    Median Imputation   
 225   XGBoost       NaN             None    Median Imputation   
 226   XGBoost       NaN             None    Median Imputation   
 227   XGBoost       NaN             None    Median Imputation   
 228   XGBoost       NaN             None    Median Imputation   
 229   XGBoost       NaN             None    Median Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 130  Hybrid Sampling (SMOTETomek)   
 131  Hybrid Sampling (SMOTETomek)   
 132  Hybrid Sampling (SMOTETomek)   
 133  Hybrid Sampling (SMOTETomek)   
 134  Hybrid Sampling (SMOTETomek)   
 135  Hybrid Sampling (SMOTETomek)   
 136  Hybrid Sampling (SMOTETomek)   
 137  Hybrid Sampling (SMOTETomek)   
 138  Hybrid Sampling (SMOTETomek)   
 139  Hybrid Sampling (SMOTETomek)   
 140  Hybrid Sampling (SMOTETomek)   
 141  Hybrid Sampling (SMOTETomek)   
 142  Hybrid Sampling (SMOTETomek)   
 143  Hybrid Sampling (SMOTETomek)   
 144  Hybrid Sampling (SMOTETomek)   
 145  Hybrid Sampling (SMOTETomek)   
 146  Hybrid Sampling (SMOTETomek)   
 147  Hybrid Sampling (SMOTETomek)   
 148  Hybrid Sampling (SMOTETomek)   
 149  Hybrid Sampling (SMOTETomek)   
 150  Hybrid Sampling (SMOTETomek)   
 151  Hybrid Sampling (SMOTETomek)   
 152  Hybrid Sampling (SMOTETomek)   
 153  Hybrid Sampling (SMOTETomek)   
 154  Hybrid Sampling (SMOTETomek)   
 155  Hybrid Sampling (SMOTETomek)   
 156  Hybrid Sampling (SMOTETomek)   
 157  Hybrid Sampling (SMOTETomek)   
 158  Hybrid Sampling (SMOTETomek)   
 159  Hybrid Sampling (SMOTETomek)   
 160  Hybrid Sampling (SMOTETomek)   
 161  Hybrid Sampling (SMOTETomek)   
 162  Hybrid Sampling (SMOTETomek)   
 163  Hybrid Sampling (SMOTETomek)   
 164  Hybrid Sampling (SMOTETomek)   
 165  Hybrid Sampling (SMOTETomek)   
 166  Hybrid Sampling (SMOTETomek)   
 167  Hybrid Sampling (SMOTETomek)   
 168  Hybrid Sampling (SMOTETomek)   
 169  Hybrid Sampling (SMOTETomek)   
 170  Hybrid Sampling (SMOTETomek)   
 171  Hybrid Sampling (SMOTETomek)   
 172  Hybrid Sampling (SMOTETomek)   
 173  Hybrid Sampling (SMOTETomek)   
 174  Hybrid Sampling (SMOTETomek)   
 175  Hybrid Sampling (SMOTETomek)   
 176  Hybrid Sampling (SMOTETomek)   
 177  Hybrid Sampling (SMOTETomek)   
 178  Hybrid Sampling (SMOTETomek)   
 179  Hybrid Sampling (SMOTETomek)   
 180  Hybrid Sampling (SMOTETomek)   
 181  Hybrid Sampling (SMOTETomek)   
 182  Hybrid Sampling (SMOTETomek)   
 183  Hybrid Sampling (SMOTETomek)   
 184  Hybrid Sampling (SMOTETomek)   
 185  Hybrid Sampling (SMOTETomek)   
 186  Hybrid Sampling (SMOTETomek)   
 187  Hybrid Sampling (SMOTETomek)   
 188  Hybrid Sampling (SMOTETomek)   
 189  Hybrid Sampling (SMOTETomek)   
 190  Hybrid Sampling (SMOTETomek)   
 191  Hybrid Sampling (SMOTETomek)   
 192  Hybrid Sampling (SMOTETomek)   
 193  Hybrid Sampling (SMOTETomek)   
 194  Hybrid Sampling (SMOTETomek)   
 195  Hybrid Sampling (SMOTETomek)   
 196  Hybrid Sampling (SMOTETomek)   
 197  Hybrid Sampling (SMOTETomek)   
 198  Hybrid Sampling (SMOTETomek)   
 199  Hybrid Sampling (SMOTETomek)   
 200  Hybrid Sampling (SMOTETomek)   
 201  Hybrid Sampling (SMOTETomek)   
 202  Hybrid Sampling (SMOTETomek)   
 203  Hybrid Sampling (SMOTETomek)   
 204  Hybrid Sampling (SMOTETomek)   
 205  Hybrid Sampling (SMOTETomek)   
 206  Hybrid Sampling (SMOTETomek)   
 207  Hybrid Sampling (SMOTETomek)   
 208  Hybrid Sampling (SMOTETomek)   
 209  Hybrid Sampling (SMOTETomek)   
 210  Hybrid Sampling (SMOTETomek)   
 211  Hybrid Sampling (SMOTETomek)   
 212  Hybrid Sampling (SMOTETomek)   
 213  Hybrid Sampling (SMOTETomek)   
 214  Hybrid Sampling (SMOTETomek)   
 215  Hybrid Sampling (SMOTETomek)   
 216  Hybrid Sampling (SMOTETomek)   
 217  Hybrid Sampling (SMOTETomek)   
 218  Hybrid Sampling (SMOTETomek)   
 219  Hybrid Sampling (SMOTETomek)   
 220  Hybrid Sampling (SMOTETomek)   
 221  Hybrid Sampling (SMOTETomek)   
 222  Hybrid Sampling (SMOTETomek)   
 223  Hybrid Sampling (SMOTETomek)   
 224  Hybrid Sampling (SMOTETomek)   
 225  Hybrid Sampling (SMOTETomek)   
 226  Hybrid Sampling (SMOTETomek)   
 227  Hybrid Sampling (SMOTETomek)   
 228  Hybrid Sampling (SMOTETomek)   
 229  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 1    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 2    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 3    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 4    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 5    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 6    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 7    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 8    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 9    XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 10   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 11   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 12   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 13   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 14   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 15   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 16   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 17   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 18   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 19   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 20   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 21   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 22   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 23   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 24   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 25   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 26   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 27   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 28   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 29   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 30   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 31   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 32   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 33   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 34   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 35   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 36   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 37   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 38   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 39   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 40   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 41   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 42   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 43   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 44   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 45   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 46   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 47   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 48   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 49   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 50   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 51   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 52   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 53   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 54   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 55   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 56   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 57   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 58   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 59   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 60   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 61   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 62   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 63   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 64   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 65   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 66   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 67   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 68   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 69   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 70   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 71   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 72   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 73   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 74   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 75   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 76   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 77   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 78   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 79   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 80   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 81   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 82   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 83   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 84   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 85   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 86   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 87   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 88   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 89   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 90   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 91   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 92   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 93   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 94   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 95   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 96   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 97   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 98   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 99   XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 100  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 101  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 102  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 103  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 104  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 105  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 106  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 107  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 108  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 109  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 110  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 111  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 112  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 113  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 114  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 115  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 116  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 117  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 118  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 119  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 120  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 121  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 122  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 123  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 124  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 125  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 126  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 127  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 128  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 129  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 130  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 131  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 132  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 133  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 134  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 135  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 136  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 137  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 138  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 139  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 140  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 141  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 142  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 143  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 144  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 145  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 146  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 147  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 148  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 149  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 150  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 151  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 152  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 153  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 154  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 155  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 156  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 157  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 158  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 159  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 160  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 161  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 162  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 163  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 164  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 165  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 166  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 167  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 168  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 169  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 170  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 171  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 172  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 173  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 174  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 175  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 176  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 177  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 178  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 179  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 180  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 181  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 182  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 183  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 184  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 185  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 186  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 187  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 188  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 189  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 190  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 191  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 192  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 193  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 194  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 195  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 196  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 197  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 198  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 199  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 200  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 201  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 202  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 203  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 204  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 205  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 206  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 207  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 208  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 209  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 210  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 211  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 212  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 213  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 214  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 215  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 216  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 217  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 218  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 219  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 220  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 221  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 222  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 223  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 224  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 225  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 226  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 227  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 228  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  
 229  XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.810903             NaN      Zero Imputation   
 1    LightGBM  0.131700             NaN      Zero Imputation   
 2    LightGBM  0.362869             NaN      Zero Imputation   
 3    LightGBM  0.193258             NaN      Zero Imputation   
 4    LightGBM  0.685294             NaN      Zero Imputation   
 5    LightGBM  0.276413             NaN      Zero Imputation   
 6    LightGBM  0.370589             NaN      Zero Imputation   
 7    LightGBM       NaN            gbdt      Zero Imputation   
 8    LightGBM       NaN            None      Zero Imputation   
 9    LightGBM       NaN             1.0      Zero Imputation   
 10   LightGBM       NaN           split      Zero Imputation   
 11   LightGBM       NaN             0.1      Zero Imputation   
 12   LightGBM       NaN              -1      Zero Imputation   
 13   LightGBM       NaN              20      Zero Imputation   
 14   LightGBM       NaN           0.001      Zero Imputation   
 15   LightGBM       NaN             0.0      Zero Imputation   
 16   LightGBM       NaN             100      Zero Imputation   
 17   LightGBM       NaN            None      Zero Imputation   
 18   LightGBM       NaN              31      Zero Imputation   
 19   LightGBM       NaN            None      Zero Imputation   
 20   LightGBM       NaN            None      Zero Imputation   
 21   LightGBM       NaN             0.0      Zero Imputation   
 22   LightGBM       NaN             0.0      Zero Imputation   
 23   LightGBM       NaN             1.0      Zero Imputation   
 24   LightGBM       NaN          200000      Zero Imputation   
 25   LightGBM       NaN               0      Zero Imputation   
 26   LightGBM  0.814854             NaN      Zero Imputation   
 27   LightGBM  0.100741             NaN      Zero Imputation   
 28   LightGBM  0.414634             NaN      Zero Imputation   
 29   LightGBM  0.162098             NaN      Zero Imputation   
 30   LightGBM  0.711087             NaN      Zero Imputation   
 31   LightGBM  0.340757             NaN      Zero Imputation   
 32   LightGBM  0.422173             NaN      Zero Imputation   
 33   LightGBM       NaN            gbdt      Zero Imputation   
 34   LightGBM       NaN            None      Zero Imputation   
 35   LightGBM       NaN             1.0      Zero Imputation   
 36   LightGBM       NaN           split      Zero Imputation   
 37   LightGBM       NaN             0.1      Zero Imputation   
 38   LightGBM       NaN              -1      Zero Imputation   
 39   LightGBM       NaN              20      Zero Imputation   
 40   LightGBM       NaN           0.001      Zero Imputation   
 41   LightGBM       NaN             0.0      Zero Imputation   
 42   LightGBM       NaN             100      Zero Imputation   
 43   LightGBM       NaN            None      Zero Imputation   
 44   LightGBM       NaN              31      Zero Imputation   
 45   LightGBM       NaN            None      Zero Imputation   
 46   LightGBM       NaN            None      Zero Imputation   
 47   LightGBM       NaN             0.0      Zero Imputation   
 48   LightGBM       NaN             0.0      Zero Imputation   
 49   LightGBM       NaN             1.0      Zero Imputation   
 50   LightGBM       NaN          200000      Zero Imputation   
 51   LightGBM       NaN               0      Zero Imputation   
 52   LightGBM  0.798789             NaN      Zero Imputation   
 53   LightGBM  0.107483             NaN      Zero Imputation   
 54   LightGBM  0.422460             NaN      Zero Imputation   
 55   LightGBM  0.171367             NaN      Zero Imputation   
 56   LightGBM  0.692921             NaN      Zero Imputation   
 57   LightGBM  0.315564             NaN      Zero Imputation   
 58   LightGBM  0.385843             NaN      Zero Imputation   
 59   LightGBM       NaN            gbdt      Zero Imputation   
 60   LightGBM       NaN            None      Zero Imputation   
 61   LightGBM       NaN             1.0      Zero Imputation   
 62   LightGBM       NaN           split      Zero Imputation   
 63   LightGBM       NaN             0.1      Zero Imputation   
 64   LightGBM       NaN              -1      Zero Imputation   
 65   LightGBM       NaN              20      Zero Imputation   
 66   LightGBM       NaN           0.001      Zero Imputation   
 67   LightGBM       NaN             0.0      Zero Imputation   
 68   LightGBM       NaN             100      Zero Imputation   
 69   LightGBM       NaN            None      Zero Imputation   
 70   LightGBM       NaN              31      Zero Imputation   
 71   LightGBM       NaN            None      Zero Imputation   
 72   LightGBM       NaN            None      Zero Imputation   
 73   LightGBM       NaN             0.0      Zero Imputation   
 74   LightGBM       NaN             0.0      Zero Imputation   
 75   LightGBM       NaN             1.0      Zero Imputation   
 76   LightGBM       NaN          200000      Zero Imputation   
 77   LightGBM       NaN               0      Zero Imputation   
 78   LightGBM  0.807429             NaN      Zero Imputation   
 79   LightGBM  0.113997             NaN      Zero Imputation   
 80   LightGBM  0.403061             NaN      Zero Imputation   
 81   LightGBM  0.177728             NaN      Zero Imputation   
 82   LightGBM  0.688001             NaN      Zero Imputation   
 83   LightGBM  0.285159             NaN      Zero Imputation   
 84   LightGBM  0.376002             NaN      Zero Imputation   
 85   LightGBM       NaN            gbdt      Zero Imputation   
 86   LightGBM       NaN            None      Zero Imputation   
 87   LightGBM       NaN             1.0      Zero Imputation   
 88   LightGBM       NaN           split      Zero Imputation   
 89   LightGBM       NaN             0.1      Zero Imputation   
 90   LightGBM       NaN              -1      Zero Imputation   
 91   LightGBM       NaN              20      Zero Imputation   
 92   LightGBM       NaN           0.001      Zero Imputation   
 93   LightGBM       NaN             0.0      Zero Imputation   
 94   LightGBM       NaN             100      Zero Imputation   
 95   LightGBM       NaN            None      Zero Imputation   
 96   LightGBM       NaN              31      Zero Imputation   
 97   LightGBM       NaN            None      Zero Imputation   
 98   LightGBM       NaN            None      Zero Imputation   
 99   LightGBM       NaN             0.0      Zero Imputation   
 100  LightGBM       NaN             0.0      Zero Imputation   
 101  LightGBM       NaN             1.0      Zero Imputation   
 102  LightGBM       NaN          200000      Zero Imputation   
 103  LightGBM       NaN               0      Zero Imputation   
 104  LightGBM  0.826660             NaN      Zero Imputation   
 105  LightGBM  0.151420             NaN      Zero Imputation   
 106  LightGBM  0.444444             NaN      Zero Imputation   
 107  LightGBM  0.225882             NaN      Zero Imputation   
 108  LightGBM  0.723786             NaN      Zero Imputation   
 109  LightGBM  0.354412             NaN      Zero Imputation   
 110  LightGBM  0.447571             NaN      Zero Imputation   
 111  LightGBM       NaN            gbdt      Zero Imputation   
 112  LightGBM       NaN            None      Zero Imputation   
 113  LightGBM       NaN             1.0      Zero Imputation   
 114  LightGBM       NaN           split      Zero Imputation   
 115  LightGBM       NaN             0.1      Zero Imputation   
 116  LightGBM       NaN              -1      Zero Imputation   
 117  LightGBM       NaN              20      Zero Imputation   
 118  LightGBM       NaN           0.001      Zero Imputation   
 119  LightGBM       NaN             0.0      Zero Imputation   
 120  LightGBM       NaN             100      Zero Imputation   
 121  LightGBM       NaN            None      Zero Imputation   
 122  LightGBM       NaN              31      Zero Imputation   
 123  LightGBM       NaN            None      Zero Imputation   
 124  LightGBM       NaN            None      Zero Imputation   
 125  LightGBM       NaN             0.0      Zero Imputation   
 126  LightGBM       NaN             0.0      Zero Imputation   
 127  LightGBM       NaN             1.0      Zero Imputation   
 128  LightGBM       NaN          200000      Zero Imputation   
 129  LightGBM       NaN               0      Zero Imputation   
 
     Imbalance Class Technique                             Model Unique Code  
 0                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 1                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 2                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 3                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 4                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 5                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 6                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 7                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 8                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 9                Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 10               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 11               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 12               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 13               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 14               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 15               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 16               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 17               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 18               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 19               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 20               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 21               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 22               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 23               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 24               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 25               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_0  
 26               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 27               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 28               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 29               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 30               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 31               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 32               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 33               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 34               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 35               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 36               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 37               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 38               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 39               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 40               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 41               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 42               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 43               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 44               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 45               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 46               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 47               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 48               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 49               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 50               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 51               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_1  
 52               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 53               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 54               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 55               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 56               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 57               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 58               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 59               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 60               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 61               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 62               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 63               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 64               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 65               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 66               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 67               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 68               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 69               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 70               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 71               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 72               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 73               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 74               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 75               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 76               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 77               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_2  
 78               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 79               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 80               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 81               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 82               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 83               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 84               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 85               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 86               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 87               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 88               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 89               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 90               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 91               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 92               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 93               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 94               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 95               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 96               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 97               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 98               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 99               Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 100              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 101              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 102              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 103              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_3  
 104              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 105              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 106              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 107              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 108              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 109              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 110              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 111              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 112              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 113              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 114              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 115              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 116              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 117              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 118              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 119              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 120              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 121              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 122              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 123              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 124              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 125              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 126              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 127              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 128              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  
 129              Oversampling  LightGBM_Oversampling_Zero Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.814854             NaN      Mean Imputation   
 1    LightGBM  0.133648             NaN      Mean Imputation   
 2    LightGBM  0.358650             NaN      Mean Imputation   
 3    LightGBM  0.194731             NaN      Mean Imputation   
 4    LightGBM  0.684344             NaN      Mean Imputation   
 5    LightGBM  0.304198             NaN      Mean Imputation   
 6    LightGBM  0.368689             NaN      Mean Imputation   
 7    LightGBM       NaN            gbdt      Mean Imputation   
 8    LightGBM       NaN            None      Mean Imputation   
 9    LightGBM       NaN             1.0      Mean Imputation   
 10   LightGBM       NaN           split      Mean Imputation   
 11   LightGBM       NaN             0.1      Mean Imputation   
 12   LightGBM       NaN              -1      Mean Imputation   
 13   LightGBM       NaN              20      Mean Imputation   
 14   LightGBM       NaN           0.001      Mean Imputation   
 15   LightGBM       NaN             0.0      Mean Imputation   
 16   LightGBM       NaN             100      Mean Imputation   
 17   LightGBM       NaN            None      Mean Imputation   
 18   LightGBM       NaN              31      Mean Imputation   
 19   LightGBM       NaN            None      Mean Imputation   
 20   LightGBM       NaN            None      Mean Imputation   
 21   LightGBM       NaN             0.0      Mean Imputation   
 22   LightGBM       NaN             0.0      Mean Imputation   
 23   LightGBM       NaN             1.0      Mean Imputation   
 24   LightGBM       NaN          200000      Mean Imputation   
 25   LightGBM       NaN               0      Mean Imputation   
 26   LightGBM  0.818804             NaN      Mean Imputation   
 27   LightGBM  0.101824             NaN      Mean Imputation   
 28   LightGBM  0.408537             NaN      Mean Imputation   
 29   LightGBM  0.163017             NaN      Mean Imputation   
 30   LightGBM  0.704826             NaN      Mean Imputation   
 31   LightGBM  0.338201             NaN      Mean Imputation   
 32   LightGBM  0.409653             NaN      Mean Imputation   
 33   LightGBM       NaN            gbdt      Mean Imputation   
 34   LightGBM       NaN            None      Mean Imputation   
 35   LightGBM       NaN             1.0      Mean Imputation   
 36   LightGBM       NaN           split      Mean Imputation   
 37   LightGBM       NaN             0.1      Mean Imputation   
 38   LightGBM       NaN              -1      Mean Imputation   
 39   LightGBM       NaN              20      Mean Imputation   
 40   LightGBM       NaN           0.001      Mean Imputation   
 41   LightGBM       NaN             0.0      Mean Imputation   
 42   LightGBM       NaN             100      Mean Imputation   
 43   LightGBM       NaN            None      Mean Imputation   
 44   LightGBM       NaN              31      Mean Imputation   
 45   LightGBM       NaN            None      Mean Imputation   
 46   LightGBM       NaN            None      Mean Imputation   
 47   LightGBM       NaN             0.0      Mean Imputation   
 48   LightGBM       NaN             0.0      Mean Imputation   
 49   LightGBM       NaN             1.0      Mean Imputation   
 50   LightGBM       NaN          200000      Mean Imputation   
 51   LightGBM       NaN               0      Mean Imputation   
 52   LightGBM  0.803266             NaN      Mean Imputation   
 53   LightGBM  0.117486             NaN      Mean Imputation   
 54   LightGBM  0.459893             NaN      Mean Imputation   
 55   LightGBM  0.187160             NaN      Mean Imputation   
 56   LightGBM  0.696535             NaN      Mean Imputation   
 57   LightGBM  0.306396             NaN      Mean Imputation   
 58   LightGBM  0.393070             NaN      Mean Imputation   
 59   LightGBM       NaN            gbdt      Mean Imputation   
 60   LightGBM       NaN            None      Mean Imputation   
 61   LightGBM       NaN             1.0      Mean Imputation   
 62   LightGBM       NaN           split      Mean Imputation   
 63   LightGBM       NaN             0.1      Mean Imputation   
 64   LightGBM       NaN              -1      Mean Imputation   
 65   LightGBM       NaN              20      Mean Imputation   
 66   LightGBM       NaN           0.001      Mean Imputation   
 67   LightGBM       NaN             0.0      Mean Imputation   
 68   LightGBM       NaN             100      Mean Imputation   
 69   LightGBM       NaN            None      Mean Imputation   
 70   LightGBM       NaN              31      Mean Imputation   
 71   LightGBM       NaN            None      Mean Imputation   
 72   LightGBM       NaN            None      Mean Imputation   
 73   LightGBM       NaN             0.0      Mean Imputation   
 74   LightGBM       NaN             0.0      Mean Imputation   
 75   LightGBM       NaN             1.0      Mean Imputation   
 76   LightGBM       NaN          200000      Mean Imputation   
 77   LightGBM       NaN               0      Mean Imputation   
 78   LightGBM  0.803477             NaN      Mean Imputation   
 79   LightGBM  0.104885             NaN      Mean Imputation   
 80   LightGBM  0.372449             NaN      Mean Imputation   
 81   LightGBM  0.163677             NaN      Mean Imputation   
 82   LightGBM  0.679731             NaN      Mean Imputation   
 83   LightGBM  0.283696             NaN      Mean Imputation   
 84   LightGBM  0.359461             NaN      Mean Imputation   
 85   LightGBM       NaN            gbdt      Mean Imputation   
 86   LightGBM       NaN            None      Mean Imputation   
 87   LightGBM       NaN             1.0      Mean Imputation   
 88   LightGBM       NaN           split      Mean Imputation   
 89   LightGBM       NaN             0.1      Mean Imputation   
 90   LightGBM       NaN              -1      Mean Imputation   
 91   LightGBM       NaN              20      Mean Imputation   
 92   LightGBM       NaN           0.001      Mean Imputation   
 93   LightGBM       NaN             0.0      Mean Imputation   
 94   LightGBM       NaN             100      Mean Imputation   
 95   LightGBM       NaN            None      Mean Imputation   
 96   LightGBM       NaN              31      Mean Imputation   
 97   LightGBM       NaN            None      Mean Imputation   
 98   LightGBM       NaN            None      Mean Imputation   
 99   LightGBM       NaN             0.0      Mean Imputation   
 100  LightGBM       NaN             0.0      Mean Imputation   
 101  LightGBM       NaN             1.0      Mean Imputation   
 102  LightGBM       NaN          200000      Mean Imputation   
 103  LightGBM       NaN               0      Mean Imputation   
 104  LightGBM  0.826660             NaN      Mean Imputation   
 105  LightGBM  0.141234             NaN      Mean Imputation   
 106  LightGBM  0.402778             NaN      Mean Imputation   
 107  LightGBM  0.209135             NaN      Mean Imputation   
 108  LightGBM  0.707874             NaN      Mean Imputation   
 109  LightGBM  0.326117             NaN      Mean Imputation   
 110  LightGBM  0.415748             NaN      Mean Imputation   
 111  LightGBM       NaN            gbdt      Mean Imputation   
 112  LightGBM       NaN            None      Mean Imputation   
 113  LightGBM       NaN             1.0      Mean Imputation   
 114  LightGBM       NaN           split      Mean Imputation   
 115  LightGBM       NaN             0.1      Mean Imputation   
 116  LightGBM       NaN              -1      Mean Imputation   
 117  LightGBM       NaN              20      Mean Imputation   
 118  LightGBM       NaN           0.001      Mean Imputation   
 119  LightGBM       NaN             0.0      Mean Imputation   
 120  LightGBM       NaN             100      Mean Imputation   
 121  LightGBM       NaN            None      Mean Imputation   
 122  LightGBM       NaN              31      Mean Imputation   
 123  LightGBM       NaN            None      Mean Imputation   
 124  LightGBM       NaN            None      Mean Imputation   
 125  LightGBM       NaN             0.0      Mean Imputation   
 126  LightGBM       NaN             0.0      Mean Imputation   
 127  LightGBM       NaN             1.0      Mean Imputation   
 128  LightGBM       NaN          200000      Mean Imputation   
 129  LightGBM       NaN               0      Mean Imputation   
 
     Imbalance Class Technique                             Model Unique Code  
 0                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 1                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 2                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 3                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 4                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 5                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 6                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 7                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 8                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 9                Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 10               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 11               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 12               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 13               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 14               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 15               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 16               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 17               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 18               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 19               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 20               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 21               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 22               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 23               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 24               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 25               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_0  
 26               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 27               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 28               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 29               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 30               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 31               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 32               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 33               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 34               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 35               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 36               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 37               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 38               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 39               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 40               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 41               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 42               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 43               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 44               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 45               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 46               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 47               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 48               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 49               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 50               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 51               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_1  
 52               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 53               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 54               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 55               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 56               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 57               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 58               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 59               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 60               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 61               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 62               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 63               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 64               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 65               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 66               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 67               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 68               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 69               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 70               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 71               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 72               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 73               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 74               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 75               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 76               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 77               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_2  
 78               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 79               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 80               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 81               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 82               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 83               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 84               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 85               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 86               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 87               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 88               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 89               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 90               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 91               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 92               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 93               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 94               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 95               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 96               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 97               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 98               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 99               Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 100              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 101              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 102              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 103              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_3  
 104              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 105              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 106              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 107              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 108              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 109              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 110              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 111              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 112              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 113              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 114              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 115              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 116              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 117              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 118              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 119              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 120              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 121              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 122              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 123              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 124              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 125              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 126              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 127              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 128              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  
 129              Oversampling  LightGBM_Oversampling_Mean Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.819331             NaN    Median Imputation   
 1    LightGBM  0.131363             NaN    Median Imputation   
 2    LightGBM  0.337553             NaN    Median Imputation   
 3    LightGBM  0.189125             NaN    Median Imputation   
 4    LightGBM  0.686851             NaN    Median Imputation   
 5    LightGBM  0.296584             NaN    Median Imputation   
 6    LightGBM  0.373702             NaN    Median Imputation   
 7    LightGBM       NaN            gbdt    Median Imputation   
 8    LightGBM       NaN            None    Median Imputation   
 9    LightGBM       NaN             1.0    Median Imputation   
 10   LightGBM       NaN           split    Median Imputation   
 11   LightGBM       NaN             0.1    Median Imputation   
 12   LightGBM       NaN              -1    Median Imputation   
 13   LightGBM       NaN              20    Median Imputation   
 14   LightGBM       NaN           0.001    Median Imputation   
 15   LightGBM       NaN             0.0    Median Imputation   
 16   LightGBM       NaN             100    Median Imputation   
 17   LightGBM       NaN            None    Median Imputation   
 18   LightGBM       NaN              31    Median Imputation   
 19   LightGBM       NaN            None    Median Imputation   
 20   LightGBM       NaN            None    Median Imputation   
 21   LightGBM       NaN             0.0    Median Imputation   
 22   LightGBM       NaN             0.0    Median Imputation   
 23   LightGBM       NaN             1.0    Median Imputation   
 24   LightGBM       NaN          200000    Median Imputation   
 25   LightGBM       NaN               0    Median Imputation   
 26   LightGBM  0.825125             NaN    Median Imputation   
 27   LightGBM  0.094156             NaN    Median Imputation   
 28   LightGBM  0.353659             NaN    Median Imputation   
 29   LightGBM  0.148718             NaN    Median Imputation   
 30   LightGBM  0.700228             NaN    Median Imputation   
 31   LightGBM  0.326580             NaN    Median Imputation   
 32   LightGBM  0.400457             NaN    Median Imputation   
 33   LightGBM       NaN            gbdt    Median Imputation   
 34   LightGBM       NaN            None    Median Imputation   
 35   LightGBM       NaN             1.0    Median Imputation   
 36   LightGBM       NaN           split    Median Imputation   
 37   LightGBM       NaN             0.1    Median Imputation   
 38   LightGBM       NaN              -1    Median Imputation   
 39   LightGBM       NaN              20    Median Imputation   
 40   LightGBM       NaN           0.001    Median Imputation   
 41   LightGBM       NaN             0.0    Median Imputation   
 42   LightGBM       NaN             100    Median Imputation   
 43   LightGBM       NaN            None    Median Imputation   
 44   LightGBM       NaN              31    Median Imputation   
 45   LightGBM       NaN            None    Median Imputation   
 46   LightGBM       NaN            None    Median Imputation   
 47   LightGBM       NaN             0.0    Median Imputation   
 48   LightGBM       NaN             0.0    Median Imputation   
 49   LightGBM       NaN             1.0    Median Imputation   
 50   LightGBM       NaN          200000    Median Imputation   
 51   LightGBM       NaN               0    Median Imputation   
 52   LightGBM  0.816434             NaN    Median Imputation   
 53   LightGBM  0.115964             NaN    Median Imputation   
 54   LightGBM  0.411765             NaN    Median Imputation   
 55   LightGBM  0.180964             NaN    Median Imputation   
 56   LightGBM  0.697232             NaN    Median Imputation   
 57   LightGBM  0.314037             NaN    Median Imputation   
 58   LightGBM  0.394464             NaN    Median Imputation   
 59   LightGBM       NaN            gbdt    Median Imputation   
 60   LightGBM       NaN            None    Median Imputation   
 61   LightGBM       NaN             1.0    Median Imputation   
 62   LightGBM       NaN           split    Median Imputation   
 63   LightGBM       NaN             0.1    Median Imputation   
 64   LightGBM       NaN              -1    Median Imputation   
 65   LightGBM       NaN              20    Median Imputation   
 66   LightGBM       NaN           0.001    Median Imputation   
 67   LightGBM       NaN             0.0    Median Imputation   
 68   LightGBM       NaN             100    Median Imputation   
 69   LightGBM       NaN            None    Median Imputation   
 70   LightGBM       NaN              31    Median Imputation   
 71   LightGBM       NaN            None    Median Imputation   
 72   LightGBM       NaN            None    Median Imputation   
 73   LightGBM       NaN             0.0    Median Imputation   
 74   LightGBM       NaN             0.0    Median Imputation   
 75   LightGBM       NaN             1.0    Median Imputation   
 76   LightGBM       NaN          200000    Median Imputation   
 77   LightGBM       NaN               0    Median Imputation   
 78   LightGBM  0.814278             NaN    Median Imputation   
 79   LightGBM  0.111450             NaN    Median Imputation   
 80   LightGBM  0.372449             NaN    Median Imputation   
 81   LightGBM  0.171563             NaN    Median Imputation   
 82   LightGBM  0.686265             NaN    Median Imputation   
 83   LightGBM  0.300584             NaN    Median Imputation   
 84   LightGBM  0.372530             NaN    Median Imputation   
 85   LightGBM       NaN            gbdt    Median Imputation   
 86   LightGBM       NaN            None    Median Imputation   
 87   LightGBM       NaN             1.0    Median Imputation   
 88   LightGBM       NaN           split    Median Imputation   
 89   LightGBM       NaN             0.1    Median Imputation   
 90   LightGBM       NaN              -1    Median Imputation   
 91   LightGBM       NaN              20    Median Imputation   
 92   LightGBM       NaN           0.001    Median Imputation   
 93   LightGBM       NaN             0.0    Median Imputation   
 94   LightGBM       NaN             100    Median Imputation   
 95   LightGBM       NaN            None    Median Imputation   
 96   LightGBM       NaN              31    Median Imputation   
 97   LightGBM       NaN            None    Median Imputation   
 98   LightGBM       NaN            None    Median Imputation   
 99   LightGBM       NaN             0.0    Median Imputation   
 100  LightGBM       NaN             0.0    Median Imputation   
 101  LightGBM       NaN             1.0    Median Imputation   
 102  LightGBM       NaN          200000    Median Imputation   
 103  LightGBM       NaN               0    Median Imputation   
 104  LightGBM  0.829557             NaN    Median Imputation   
 105  LightGBM  0.137815             NaN    Median Imputation   
 106  LightGBM  0.379630             NaN    Median Imputation   
 107  LightGBM  0.202219             NaN    Median Imputation   
 108  LightGBM  0.708282             NaN    Median Imputation   
 109  LightGBM  0.338816             NaN    Median Imputation   
 110  LightGBM  0.416565             NaN    Median Imputation   
 111  LightGBM       NaN            gbdt    Median Imputation   
 112  LightGBM       NaN            None    Median Imputation   
 113  LightGBM       NaN             1.0    Median Imputation   
 114  LightGBM       NaN           split    Median Imputation   
 115  LightGBM       NaN             0.1    Median Imputation   
 116  LightGBM       NaN              -1    Median Imputation   
 117  LightGBM       NaN              20    Median Imputation   
 118  LightGBM       NaN           0.001    Median Imputation   
 119  LightGBM       NaN             0.0    Median Imputation   
 120  LightGBM       NaN             100    Median Imputation   
 121  LightGBM       NaN            None    Median Imputation   
 122  LightGBM       NaN              31    Median Imputation   
 123  LightGBM       NaN            None    Median Imputation   
 124  LightGBM       NaN            None    Median Imputation   
 125  LightGBM       NaN             0.0    Median Imputation   
 126  LightGBM       NaN             0.0    Median Imputation   
 127  LightGBM       NaN             1.0    Median Imputation   
 128  LightGBM       NaN          200000    Median Imputation   
 129  LightGBM       NaN               0    Median Imputation   
 
     Imbalance Class Technique                               Model Unique Code  
 0                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 1                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 2                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 3                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 4                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 5                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 6                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 7                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 8                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 9                Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 10               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 11               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 12               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 13               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 14               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 15               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 16               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 17               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 18               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 19               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 20               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 21               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 22               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 23               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 24               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 25               Oversampling  LightGBM_Oversampling_Median Imputation_fold_0  
 26               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 27               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 28               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 29               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 30               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 31               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 32               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 33               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 34               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 35               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 36               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 37               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 38               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 39               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 40               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 41               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 42               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 43               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 44               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 45               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 46               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 47               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 48               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 49               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 50               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 51               Oversampling  LightGBM_Oversampling_Median Imputation_fold_1  
 52               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 53               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 54               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 55               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 56               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 57               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 58               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 59               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 60               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 61               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 62               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 63               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 64               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 65               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 66               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 67               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 68               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 69               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 70               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 71               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 72               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 73               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 74               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 75               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 76               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 77               Oversampling  LightGBM_Oversampling_Median Imputation_fold_2  
 78               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 79               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 80               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 81               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 82               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 83               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 84               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 85               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 86               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 87               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 88               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 89               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 90               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 91               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 92               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 93               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 94               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 95               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 96               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 97               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 98               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 99               Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 100              Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 101              Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 102              Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 103              Oversampling  LightGBM_Oversampling_Median Imputation_fold_3  
 104              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 105              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 106              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 107              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 108              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 109              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 110              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 111              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 112              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 113              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 114              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 115              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 116              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 117              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 118              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 119              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 120              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 121              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 122              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 123              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 124              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 125              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 126              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 127              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 128              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  
 129              Oversampling  LightGBM_Oversampling_Median Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.651304             NaN      Zero Imputation   
 1    LightGBM  0.109274             NaN      Zero Imputation   
 2    LightGBM  0.641350             NaN      Zero Imputation   
 3    LightGBM  0.186732             NaN      Zero Imputation   
 4    LightGBM  0.683991             NaN      Zero Imputation   
 5    LightGBM  0.316182             NaN      Zero Imputation   
 6    LightGBM  0.367982             NaN      Zero Imputation   
 7    LightGBM       NaN            gbdt      Zero Imputation   
 8    LightGBM       NaN            None      Zero Imputation   
 9    LightGBM       NaN             1.0      Zero Imputation   
 10   LightGBM       NaN           split      Zero Imputation   
 11   LightGBM       NaN             0.1      Zero Imputation   
 12   LightGBM       NaN              -1      Zero Imputation   
 13   LightGBM       NaN              20      Zero Imputation   
 14   LightGBM       NaN           0.001      Zero Imputation   
 15   LightGBM       NaN             0.0      Zero Imputation   
 16   LightGBM       NaN             100      Zero Imputation   
 17   LightGBM       NaN            None      Zero Imputation   
 18   LightGBM       NaN              31      Zero Imputation   
 19   LightGBM       NaN            None      Zero Imputation   
 20   LightGBM       NaN            None      Zero Imputation   
 21   LightGBM       NaN             0.0      Zero Imputation   
 22   LightGBM       NaN             0.0      Zero Imputation   
 23   LightGBM       NaN             1.0      Zero Imputation   
 24   LightGBM       NaN          200000      Zero Imputation   
 25   LightGBM       NaN               0      Zero Imputation   
 26   LightGBM  0.608112             NaN      Zero Imputation   
 27   LightGBM  0.075096             NaN      Zero Imputation   
 28   LightGBM  0.713415             NaN      Zero Imputation   
 29   LightGBM  0.135889             NaN      Zero Imputation   
 30   LightGBM  0.698543             NaN      Zero Imputation   
 31   LightGBM  0.325059             NaN      Zero Imputation   
 32   LightGBM  0.397087             NaN      Zero Imputation   
 33   LightGBM       NaN            gbdt      Zero Imputation   
 34   LightGBM       NaN            None      Zero Imputation   
 35   LightGBM       NaN             1.0      Zero Imputation   
 36   LightGBM       NaN           split      Zero Imputation   
 37   LightGBM       NaN             0.1      Zero Imputation   
 38   LightGBM       NaN              -1      Zero Imputation   
 39   LightGBM       NaN              20      Zero Imputation   
 40   LightGBM       NaN           0.001      Zero Imputation   
 41   LightGBM       NaN             0.0      Zero Imputation   
 42   LightGBM       NaN             100      Zero Imputation   
 43   LightGBM       NaN            None      Zero Imputation   
 44   LightGBM       NaN              31      Zero Imputation   
 45   LightGBM       NaN            None      Zero Imputation   
 46   LightGBM       NaN            None      Zero Imputation   
 47   LightGBM       NaN             0.0      Zero Imputation   
 48   LightGBM       NaN             0.0      Zero Imputation   
 49   LightGBM       NaN             1.0      Zero Imputation   
 50   LightGBM       NaN          200000      Zero Imputation   
 51   LightGBM       NaN               0      Zero Imputation   
 52   LightGBM  0.621017             NaN      Zero Imputation   
 53   LightGBM  0.082667             NaN      Zero Imputation   
 54   LightGBM  0.663102             NaN      Zero Imputation   
 55   LightGBM  0.147007             NaN      Zero Imputation   
 56   LightGBM  0.681124             NaN      Zero Imputation   
 57   LightGBM  0.304690             NaN      Zero Imputation   
 58   LightGBM  0.362248             NaN      Zero Imputation   
 59   LightGBM       NaN            gbdt      Zero Imputation   
 60   LightGBM       NaN            None      Zero Imputation   
 61   LightGBM       NaN             1.0      Zero Imputation   
 62   LightGBM       NaN           split      Zero Imputation   
 63   LightGBM       NaN             0.1      Zero Imputation   
 64   LightGBM       NaN              -1      Zero Imputation   
 65   LightGBM       NaN              20      Zero Imputation   
 66   LightGBM       NaN           0.001      Zero Imputation   
 67   LightGBM       NaN             0.0      Zero Imputation   
 68   LightGBM       NaN             100      Zero Imputation   
 69   LightGBM       NaN            None      Zero Imputation   
 70   LightGBM       NaN              31      Zero Imputation   
 71   LightGBM       NaN            None      Zero Imputation   
 72   LightGBM       NaN            None      Zero Imputation   
 73   LightGBM       NaN             0.0      Zero Imputation   
 74   LightGBM       NaN             0.0      Zero Imputation   
 75   LightGBM       NaN             1.0      Zero Imputation   
 76   LightGBM       NaN          200000      Zero Imputation   
 77   LightGBM       NaN               0      Zero Imputation   
 78   LightGBM  0.635142             NaN      Zero Imputation   
 79   LightGBM  0.083392             NaN      Zero Imputation   
 80   LightGBM  0.607143             NaN      Zero Imputation   
 81   LightGBM  0.146642             NaN      Zero Imputation   
 82   LightGBM  0.661387             NaN      Zero Imputation   
 83   LightGBM  0.278696             NaN      Zero Imputation   
 84   LightGBM  0.322775             NaN      Zero Imputation   
 85   LightGBM       NaN            gbdt      Zero Imputation   
 86   LightGBM       NaN            None      Zero Imputation   
 87   LightGBM       NaN             1.0      Zero Imputation   
 88   LightGBM       NaN           split      Zero Imputation   
 89   LightGBM       NaN             0.1      Zero Imputation   
 90   LightGBM       NaN              -1      Zero Imputation   
 91   LightGBM       NaN              20      Zero Imputation   
 92   LightGBM       NaN           0.001      Zero Imputation   
 93   LightGBM       NaN             0.0      Zero Imputation   
 94   LightGBM       NaN             100      Zero Imputation   
 95   LightGBM       NaN            None      Zero Imputation   
 96   LightGBM       NaN              31      Zero Imputation   
 97   LightGBM       NaN            None      Zero Imputation   
 98   LightGBM       NaN            None      Zero Imputation   
 99   LightGBM       NaN             0.0      Zero Imputation   
 100  LightGBM       NaN             0.0      Zero Imputation   
 101  LightGBM       NaN             1.0      Zero Imputation   
 102  LightGBM       NaN          200000      Zero Imputation   
 103  LightGBM       NaN               0      Zero Imputation   
 104  LightGBM  0.637513             NaN      Zero Imputation   
 105  LightGBM  0.093268             NaN      Zero Imputation   
 106  LightGBM  0.615741             NaN      Zero Imputation   
 107  LightGBM  0.161998             NaN      Zero Imputation   
 108  LightGBM  0.676729             NaN      Zero Imputation   
 109  LightGBM  0.272434             NaN      Zero Imputation   
 110  LightGBM  0.353458             NaN      Zero Imputation   
 111  LightGBM       NaN            gbdt      Zero Imputation   
 112  LightGBM       NaN            None      Zero Imputation   
 113  LightGBM       NaN             1.0      Zero Imputation   
 114  LightGBM       NaN           split      Zero Imputation   
 115  LightGBM       NaN             0.1      Zero Imputation   
 116  LightGBM       NaN              -1      Zero Imputation   
 117  LightGBM       NaN              20      Zero Imputation   
 118  LightGBM       NaN           0.001      Zero Imputation   
 119  LightGBM       NaN             0.0      Zero Imputation   
 120  LightGBM       NaN             100      Zero Imputation   
 121  LightGBM       NaN            None      Zero Imputation   
 122  LightGBM       NaN              31      Zero Imputation   
 123  LightGBM       NaN            None      Zero Imputation   
 124  LightGBM       NaN            None      Zero Imputation   
 125  LightGBM       NaN             0.0      Zero Imputation   
 126  LightGBM       NaN             0.0      Zero Imputation   
 127  LightGBM       NaN             1.0      Zero Imputation   
 128  LightGBM       NaN          200000      Zero Imputation   
 129  LightGBM       NaN               0      Zero Imputation   
 
     Imbalance Class Technique                              Model Unique Code  
 0               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 1               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 2               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 3               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 4               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 5               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 6               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 7               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 8               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 9               Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 10              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 11              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 12              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 13              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 14              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 15              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 16              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 17              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 18              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 19              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 20              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 21              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 22              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 23              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 24              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 25              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_0  
 26              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 27              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 28              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 29              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 30              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 31              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 32              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 33              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 34              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 35              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 36              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 37              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 38              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 39              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 40              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 41              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 42              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 43              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 44              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 45              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 46              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 47              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 48              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 49              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 50              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 51              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_1  
 52              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 53              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 54              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 55              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 56              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 57              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 58              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 59              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 60              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 61              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 62              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 63              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 64              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 65              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 66              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 67              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 68              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 69              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 70              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 71              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 72              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 73              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 74              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 75              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 76              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 77              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_2  
 78              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 79              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 80              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 81              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 82              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 83              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 84              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 85              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 86              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 87              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 88              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 89              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 90              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 91              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 92              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 93              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 94              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 95              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 96              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 97              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 98              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 99              Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 100             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 101             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 102             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 103             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_3  
 104             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 105             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 106             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 107             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 108             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 109             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 110             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 111             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 112             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 113             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 114             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 115             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 116             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 117             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 118             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 119             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 120             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 121             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 122             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 123             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 124             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 125             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 126             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 127             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 128             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  
 129             Undersampling  LightGBM_Undersampling_Zero Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.648933             NaN      Mean Imputation   
 1    LightGBM  0.109687             NaN      Mean Imputation   
 2    LightGBM  0.649789             NaN      Mean Imputation   
 3    LightGBM  0.187690             NaN      Mean Imputation   
 4    LightGBM  0.690047             NaN      Mean Imputation   
 5    LightGBM  0.320120             NaN      Mean Imputation   
 6    LightGBM  0.380094             NaN      Mean Imputation   
 7    LightGBM       NaN            gbdt      Mean Imputation   
 8    LightGBM       NaN            None      Mean Imputation   
 9    LightGBM       NaN             1.0      Mean Imputation   
 10   LightGBM       NaN           split      Mean Imputation   
 11   LightGBM       NaN             0.1      Mean Imputation   
 12   LightGBM       NaN              -1      Mean Imputation   
 13   LightGBM       NaN              20      Mean Imputation   
 14   LightGBM       NaN           0.001      Mean Imputation   
 15   LightGBM       NaN             0.0      Mean Imputation   
 16   LightGBM       NaN             100      Mean Imputation   
 17   LightGBM       NaN            None      Mean Imputation   
 18   LightGBM       NaN              31      Mean Imputation   
 19   LightGBM       NaN            None      Mean Imputation   
 20   LightGBM       NaN            None      Mean Imputation   
 21   LightGBM       NaN             0.0      Mean Imputation   
 22   LightGBM       NaN             0.0      Mean Imputation   
 23   LightGBM       NaN             1.0      Mean Imputation   
 24   LightGBM       NaN          200000      Mean Imputation   
 25   LightGBM       NaN               0      Mean Imputation   
 26   LightGBM  0.632341             NaN      Mean Imputation   
 27   LightGBM  0.072816             NaN      Mean Imputation   
 28   LightGBM  0.640244             NaN      Mean Imputation   
 29   LightGBM  0.130760             NaN      Mean Imputation   
 30   LightGBM  0.674177             NaN      Mean Imputation   
 31   LightGBM  0.293255             NaN      Mean Imputation   
 32   LightGBM  0.348353             NaN      Mean Imputation   
 33   LightGBM       NaN            gbdt      Mean Imputation   
 34   LightGBM       NaN            None      Mean Imputation   
 35   LightGBM       NaN             1.0      Mean Imputation   
 36   LightGBM       NaN           split      Mean Imputation   
 37   LightGBM       NaN             0.1      Mean Imputation   
 38   LightGBM       NaN              -1      Mean Imputation   
 39   LightGBM       NaN              20      Mean Imputation   
 40   LightGBM       NaN           0.001      Mean Imputation   
 41   LightGBM       NaN             0.0      Mean Imputation   
 42   LightGBM       NaN             100      Mean Imputation   
 43   LightGBM       NaN            None      Mean Imputation   
 44   LightGBM       NaN              31      Mean Imputation   
 45   LightGBM       NaN            None      Mean Imputation   
 46   LightGBM       NaN            None      Mean Imputation   
 47   LightGBM       NaN             0.0      Mean Imputation   
 48   LightGBM       NaN             0.0      Mean Imputation   
 49   LightGBM       NaN             1.0      Mean Imputation   
 50   LightGBM       NaN          200000      Mean Imputation   
 51   LightGBM       NaN               0      Mean Imputation   
 52   LightGBM  0.625494             NaN      Mean Imputation   
 53   LightGBM  0.085848             NaN      Mean Imputation   
 54   LightGBM  0.684492             NaN      Mean Imputation   
 55   LightGBM  0.152563             NaN      Mean Imputation   
 56   LightGBM  0.693735             NaN      Mean Imputation   
 57   LightGBM  0.313301             NaN      Mean Imputation   
 58   LightGBM  0.387469             NaN      Mean Imputation   
 59   LightGBM       NaN            gbdt      Mean Imputation   
 60   LightGBM       NaN            None      Mean Imputation   
 61   LightGBM       NaN             1.0      Mean Imputation   
 62   LightGBM       NaN           split      Mean Imputation   
 63   LightGBM       NaN             0.1      Mean Imputation   
 64   LightGBM       NaN              -1      Mean Imputation   
 65   LightGBM       NaN              20      Mean Imputation   
 66   LightGBM       NaN           0.001      Mean Imputation   
 67   LightGBM       NaN             0.0      Mean Imputation   
 68   LightGBM       NaN             100      Mean Imputation   
 69   LightGBM       NaN            None      Mean Imputation   
 70   LightGBM       NaN              31      Mean Imputation   
 71   LightGBM       NaN            None      Mean Imputation   
 72   LightGBM       NaN            None      Mean Imputation   
 73   LightGBM       NaN             0.0      Mean Imputation   
 74   LightGBM       NaN             0.0      Mean Imputation   
 75   LightGBM       NaN             1.0      Mean Imputation   
 76   LightGBM       NaN          200000      Mean Imputation   
 77   LightGBM       NaN               0      Mean Imputation   
 78   LightGBM  0.630927             NaN      Mean Imputation   
 79   LightGBM  0.085911             NaN      Mean Imputation   
 80   LightGBM  0.637755             NaN      Mean Imputation   
 81   LightGBM  0.151423             NaN      Mean Imputation   
 82   LightGBM  0.672191             NaN      Mean Imputation   
 83   LightGBM  0.283951             NaN      Mean Imputation   
 84   LightGBM  0.344382             NaN      Mean Imputation   
 85   LightGBM       NaN            gbdt      Mean Imputation   
 86   LightGBM       NaN            None      Mean Imputation   
 87   LightGBM       NaN             1.0      Mean Imputation   
 88   LightGBM       NaN           split      Mean Imputation   
 89   LightGBM       NaN             0.1      Mean Imputation   
 90   LightGBM       NaN              -1      Mean Imputation   
 91   LightGBM       NaN              20      Mean Imputation   
 92   LightGBM       NaN           0.001      Mean Imputation   
 93   LightGBM       NaN             0.0      Mean Imputation   
 94   LightGBM       NaN             100      Mean Imputation   
 95   LightGBM       NaN            None      Mean Imputation   
 96   LightGBM       NaN              31      Mean Imputation   
 97   LightGBM       NaN            None      Mean Imputation   
 98   LightGBM       NaN            None      Mean Imputation   
 99   LightGBM       NaN             0.0      Mean Imputation   
 100  LightGBM       NaN             0.0      Mean Imputation   
 101  LightGBM       NaN             1.0      Mean Imputation   
 102  LightGBM       NaN          200000      Mean Imputation   
 103  LightGBM       NaN               0      Mean Imputation   
 104  LightGBM  0.661222             NaN      Mean Imputation   
 105  LightGBM  0.101935             NaN      Mean Imputation   
 106  LightGBM  0.634259             NaN      Mean Imputation   
 107  LightGBM  0.175641             NaN      Mean Imputation   
 108  LightGBM  0.700924             NaN      Mean Imputation   
 109  LightGBM  0.300735             NaN      Mean Imputation   
 110  LightGBM  0.401848             NaN      Mean Imputation   
 111  LightGBM       NaN            gbdt      Mean Imputation   
 112  LightGBM       NaN            None      Mean Imputation   
 113  LightGBM       NaN             1.0      Mean Imputation   
 114  LightGBM       NaN           split      Mean Imputation   
 115  LightGBM       NaN             0.1      Mean Imputation   
 116  LightGBM       NaN              -1      Mean Imputation   
 117  LightGBM       NaN              20      Mean Imputation   
 118  LightGBM       NaN           0.001      Mean Imputation   
 119  LightGBM       NaN             0.0      Mean Imputation   
 120  LightGBM       NaN             100      Mean Imputation   
 121  LightGBM       NaN            None      Mean Imputation   
 122  LightGBM       NaN              31      Mean Imputation   
 123  LightGBM       NaN            None      Mean Imputation   
 124  LightGBM       NaN            None      Mean Imputation   
 125  LightGBM       NaN             0.0      Mean Imputation   
 126  LightGBM       NaN             0.0      Mean Imputation   
 127  LightGBM       NaN             1.0      Mean Imputation   
 128  LightGBM       NaN          200000      Mean Imputation   
 129  LightGBM       NaN               0      Mean Imputation   
 
     Imbalance Class Technique                              Model Unique Code  
 0               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 1               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 2               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 3               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 4               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 5               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 6               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 7               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 8               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 9               Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 10              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 11              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 12              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 13              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 14              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 15              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 16              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 17              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 18              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 19              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 20              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 21              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 22              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 23              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 24              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 25              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_0  
 26              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 27              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 28              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 29              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 30              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 31              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 32              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 33              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 34              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 35              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 36              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 37              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 38              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 39              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 40              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 41              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 42              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 43              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 44              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 45              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 46              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 47              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 48              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 49              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 50              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 51              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_1  
 52              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 53              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 54              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 55              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 56              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 57              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 58              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 59              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 60              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 61              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 62              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 63              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 64              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 65              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 66              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 67              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 68              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 69              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 70              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 71              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 72              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 73              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 74              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 75              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 76              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 77              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_2  
 78              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 79              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 80              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 81              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 82              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 83              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 84              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 85              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 86              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 87              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 88              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 89              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 90              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 91              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 92              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 93              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 94              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 95              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 96              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 97              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 98              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 99              Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 100             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 101             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 102             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 103             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_3  
 104             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 105             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 106             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 107             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 108             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 109             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 110             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 111             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 112             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 113             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 114             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 115             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 116             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 117             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 118             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 119             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 120             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 121             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 122             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 123             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 124             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 125             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 126             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 127             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 128             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  
 129             Undersampling  LightGBM_Undersampling_Mean Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.641559             NaN    Median Imputation   
 1    LightGBM  0.104225             NaN    Median Imputation   
 2    LightGBM  0.624473             NaN    Median Imputation   
 3    LightGBM  0.178636             NaN    Median Imputation   
 4    LightGBM  0.686368             NaN    Median Imputation   
 5    LightGBM  0.291593             NaN    Median Imputation   
 6    LightGBM  0.372735             NaN    Median Imputation   
 7    LightGBM       NaN            gbdt    Median Imputation   
 8    LightGBM       NaN            None    Median Imputation   
 9    LightGBM       NaN             1.0    Median Imputation   
 10   LightGBM       NaN           split    Median Imputation   
 11   LightGBM       NaN             0.1    Median Imputation   
 12   LightGBM       NaN              -1    Median Imputation   
 13   LightGBM       NaN              20    Median Imputation   
 14   LightGBM       NaN           0.001    Median Imputation   
 15   LightGBM       NaN             0.0    Median Imputation   
 16   LightGBM       NaN             100    Median Imputation   
 17   LightGBM       NaN            None    Median Imputation   
 18   LightGBM       NaN              31    Median Imputation   
 19   LightGBM       NaN            None    Median Imputation   
 20   LightGBM       NaN            None    Median Imputation   
 21   LightGBM       NaN             0.0    Median Imputation   
 22   LightGBM       NaN             0.0    Median Imputation   
 23   LightGBM       NaN             1.0    Median Imputation   
 24   LightGBM       NaN          200000    Median Imputation   
 25   LightGBM       NaN               0    Median Imputation   
 26   LightGBM  0.632078             NaN    Median Imputation   
 27   LightGBM  0.073356             NaN    Median Imputation   
 28   LightGBM  0.646341             NaN    Median Imputation   
 29   LightGBM  0.131759             NaN    Median Imputation   
 30   LightGBM  0.695265             NaN    Median Imputation   
 31   LightGBM  0.324257             NaN    Median Imputation   
 32   LightGBM  0.390531             NaN    Median Imputation   
 33   LightGBM       NaN            gbdt    Median Imputation   
 34   LightGBM       NaN            None    Median Imputation   
 35   LightGBM       NaN             1.0    Median Imputation   
 36   LightGBM       NaN           split    Median Imputation   
 37   LightGBM       NaN             0.1    Median Imputation   
 38   LightGBM       NaN              -1    Median Imputation   
 39   LightGBM       NaN              20    Median Imputation   
 40   LightGBM       NaN           0.001    Median Imputation   
 41   LightGBM       NaN             0.0    Median Imputation   
 42   LightGBM       NaN             100    Median Imputation   
 43   LightGBM       NaN            None    Median Imputation   
 44   LightGBM       NaN              31    Median Imputation   
 45   LightGBM       NaN            None    Median Imputation   
 46   LightGBM       NaN            None    Median Imputation   
 47   LightGBM       NaN             0.0    Median Imputation   
 48   LightGBM       NaN             0.0    Median Imputation   
 49   LightGBM       NaN             1.0    Median Imputation   
 50   LightGBM       NaN          200000    Median Imputation   
 51   LightGBM       NaN               0    Median Imputation   
 52   LightGBM  0.637082             NaN    Median Imputation   
 53   LightGBM  0.080338             NaN    Median Imputation   
 54   LightGBM  0.609626             NaN    Median Imputation   
 55   LightGBM  0.141968             NaN    Median Imputation   
 56   LightGBM  0.662575             NaN    Median Imputation   
 57   LightGBM  0.260352             NaN    Median Imputation   
 58   LightGBM  0.325150             NaN    Median Imputation   
 59   LightGBM       NaN            gbdt    Median Imputation   
 60   LightGBM       NaN            None    Median Imputation   
 61   LightGBM       NaN             1.0    Median Imputation   
 62   LightGBM       NaN           split    Median Imputation   
 63   LightGBM       NaN             0.1    Median Imputation   
 64   LightGBM       NaN              -1    Median Imputation   
 65   LightGBM       NaN              20    Median Imputation   
 66   LightGBM       NaN           0.001    Median Imputation   
 67   LightGBM       NaN             0.0    Median Imputation   
 68   LightGBM       NaN             100    Median Imputation   
 69   LightGBM       NaN            None    Median Imputation   
 70   LightGBM       NaN              31    Median Imputation   
 71   LightGBM       NaN            None    Median Imputation   
 72   LightGBM       NaN            None    Median Imputation   
 73   LightGBM       NaN             0.0    Median Imputation   
 74   LightGBM       NaN             0.0    Median Imputation   
 75   LightGBM       NaN             1.0    Median Imputation   
 76   LightGBM       NaN          200000    Median Imputation   
 77   LightGBM       NaN               0    Median Imputation   
 78   LightGBM  0.625132             NaN    Median Imputation   
 79   LightGBM  0.081800             NaN    Median Imputation   
 80   LightGBM  0.612245             NaN    Median Imputation   
 81   LightGBM  0.144317             NaN    Median Imputation   
 82   LightGBM  0.674609             NaN    Median Imputation   
 83   LightGBM  0.277749             NaN    Median Imputation   
 84   LightGBM  0.349218             NaN    Median Imputation   
 85   LightGBM       NaN            gbdt    Median Imputation   
 86   LightGBM       NaN            None    Median Imputation   
 87   LightGBM       NaN             1.0    Median Imputation   
 88   LightGBM       NaN           split    Median Imputation   
 89   LightGBM       NaN             0.1    Median Imputation   
 90   LightGBM       NaN              -1    Median Imputation   
 91   LightGBM       NaN              20    Median Imputation   
 92   LightGBM       NaN           0.001    Median Imputation   
 93   LightGBM       NaN             0.0    Median Imputation   
 94   LightGBM       NaN             100    Median Imputation   
 95   LightGBM       NaN            None    Median Imputation   
 96   LightGBM       NaN              31    Median Imputation   
 97   LightGBM       NaN            None    Median Imputation   
 98   LightGBM       NaN            None    Median Imputation   
 99   LightGBM       NaN             0.0    Median Imputation   
 100  LightGBM       NaN             0.0    Median Imputation   
 101  LightGBM       NaN             1.0    Median Imputation   
 102  LightGBM       NaN          200000    Median Imputation   
 103  LightGBM       NaN               0    Median Imputation   
 104  LightGBM  0.639621             NaN    Median Imputation   
 105  LightGBM  0.098886             NaN    Median Imputation   
 106  LightGBM  0.657407             NaN    Median Imputation   
 107  LightGBM  0.171913             NaN    Median Imputation   
 108  LightGBM  0.699186             NaN    Median Imputation   
 109  LightGBM  0.315849             NaN    Median Imputation   
 110  LightGBM  0.398372             NaN    Median Imputation   
 111  LightGBM       NaN            gbdt    Median Imputation   
 112  LightGBM       NaN            None    Median Imputation   
 113  LightGBM       NaN             1.0    Median Imputation   
 114  LightGBM       NaN           split    Median Imputation   
 115  LightGBM       NaN             0.1    Median Imputation   
 116  LightGBM       NaN              -1    Median Imputation   
 117  LightGBM       NaN              20    Median Imputation   
 118  LightGBM       NaN           0.001    Median Imputation   
 119  LightGBM       NaN             0.0    Median Imputation   
 120  LightGBM       NaN             100    Median Imputation   
 121  LightGBM       NaN            None    Median Imputation   
 122  LightGBM       NaN              31    Median Imputation   
 123  LightGBM       NaN            None    Median Imputation   
 124  LightGBM       NaN            None    Median Imputation   
 125  LightGBM       NaN             0.0    Median Imputation   
 126  LightGBM       NaN             0.0    Median Imputation   
 127  LightGBM       NaN             1.0    Median Imputation   
 128  LightGBM       NaN          200000    Median Imputation   
 129  LightGBM       NaN               0    Median Imputation   
 
     Imbalance Class Technique                                Model Unique Code  
 0               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 1               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 2               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 3               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 4               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 5               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 6               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 7               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 8               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 9               Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 10              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 11              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 12              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 13              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 14              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 15              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 16              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 17              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 18              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 19              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 20              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 21              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 22              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 23              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 24              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 25              Undersampling  LightGBM_Undersampling_Median Imputation_fold_0  
 26              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 27              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 28              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 29              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 30              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 31              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 32              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 33              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 34              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 35              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 36              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 37              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 38              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 39              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 40              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 41              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 42              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 43              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 44              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 45              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 46              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 47              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 48              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 49              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 50              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 51              Undersampling  LightGBM_Undersampling_Median Imputation_fold_1  
 52              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 53              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 54              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 55              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 56              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 57              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 58              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 59              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 60              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 61              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 62              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 63              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 64              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 65              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 66              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 67              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 68              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 69              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 70              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 71              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 72              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 73              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 74              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 75              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 76              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 77              Undersampling  LightGBM_Undersampling_Median Imputation_fold_2  
 78              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 79              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 80              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 81              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 82              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 83              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 84              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 85              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 86              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 87              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 88              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 89              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 90              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 91              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 92              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 93              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 94              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 95              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 96              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 97              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 98              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 99              Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 100             Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 101             Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 102             Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 103             Undersampling  LightGBM_Undersampling_Median Imputation_fold_3  
 104             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 105             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 106             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 107             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 108             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 109             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 110             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 111             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 112             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 113             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 114             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 115             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 116             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 117             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 118             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 119             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 120             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 121             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 122             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 123             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 124             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 125             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 126             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 127             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 128             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  
 129             Undersampling  LightGBM_Undersampling_Median Imputation_fold_4  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.877008             NaN      Zero Imputation   
 1    LightGBM  0.147239             NaN      Zero Imputation   
 2    LightGBM  0.202532             NaN      Zero Imputation   
 3    LightGBM  0.170515             NaN      Zero Imputation   
 4    LightGBM  0.684273             NaN      Zero Imputation   
 5    LightGBM  0.297033             NaN      Zero Imputation   
 6    LightGBM  0.368546             NaN      Zero Imputation   
 7    LightGBM       NaN            gbdt      Zero Imputation   
 8    LightGBM       NaN            None      Zero Imputation   
 9    LightGBM       NaN             1.0      Zero Imputation   
 10   LightGBM       NaN           split      Zero Imputation   
 11   LightGBM       NaN             0.1      Zero Imputation   
 12   LightGBM       NaN              -1      Zero Imputation   
 13   LightGBM       NaN              20      Zero Imputation   
 14   LightGBM       NaN           0.001      Zero Imputation   
 15   LightGBM       NaN             0.0      Zero Imputation   
 16   LightGBM       NaN             100      Zero Imputation   
 17   LightGBM       NaN            None      Zero Imputation   
 18   LightGBM       NaN              31      Zero Imputation   
 19   LightGBM       NaN            None      Zero Imputation   
 20   LightGBM       NaN            None      Zero Imputation   
 21   LightGBM       NaN             0.0      Zero Imputation   
 22   LightGBM       NaN             0.0      Zero Imputation   
 23   LightGBM       NaN             1.0      Zero Imputation   
 24   LightGBM       NaN          200000      Zero Imputation   
 25   LightGBM       NaN               0      Zero Imputation   
 26   LightGBM  0.890703             NaN      Zero Imputation   
 27   LightGBM  0.111455             NaN      Zero Imputation   
 28   LightGBM  0.219512             NaN      Zero Imputation   
 29   LightGBM  0.147844             NaN      Zero Imputation   
 30   LightGBM  0.709188             NaN      Zero Imputation   
 31   LightGBM  0.342264             NaN      Zero Imputation   
 32   LightGBM  0.418377             NaN      Zero Imputation   
 33   LightGBM       NaN            gbdt      Zero Imputation   
 34   LightGBM       NaN            None      Zero Imputation   
 35   LightGBM       NaN             1.0      Zero Imputation   
 36   LightGBM       NaN           split      Zero Imputation   
 37   LightGBM       NaN             0.1      Zero Imputation   
 38   LightGBM       NaN              -1      Zero Imputation   
 39   LightGBM       NaN              20      Zero Imputation   
 40   LightGBM       NaN           0.001      Zero Imputation   
 41   LightGBM       NaN             0.0      Zero Imputation   
 42   LightGBM       NaN             100      Zero Imputation   
 43   LightGBM       NaN            None      Zero Imputation   
 44   LightGBM       NaN              31      Zero Imputation   
 45   LightGBM       NaN            None      Zero Imputation   
 46   LightGBM       NaN            None      Zero Imputation   
 47   LightGBM       NaN             0.0      Zero Imputation   
 48   LightGBM       NaN             0.0      Zero Imputation   
 49   LightGBM       NaN             1.0      Zero Imputation   
 50   LightGBM       NaN          200000      Zero Imputation   
 51   LightGBM       NaN               0      Zero Imputation   
 52   LightGBM  0.885963             NaN      Zero Imputation   
 53   LightGBM  0.105769             NaN      Zero Imputation   
 54   LightGBM  0.176471             NaN      Zero Imputation   
 55   LightGBM  0.132265             NaN      Zero Imputation   
 56   LightGBM  0.676592             NaN      Zero Imputation   
 57   LightGBM  0.265919             NaN      Zero Imputation   
 58   LightGBM  0.353184             NaN      Zero Imputation   
 59   LightGBM       NaN            gbdt      Zero Imputation   
 60   LightGBM       NaN            None      Zero Imputation   
 61   LightGBM       NaN             1.0      Zero Imputation   
 62   LightGBM       NaN           split      Zero Imputation   
 63   LightGBM       NaN             0.1      Zero Imputation   
 64   LightGBM       NaN              -1      Zero Imputation   
 65   LightGBM       NaN              20      Zero Imputation   
 66   LightGBM       NaN           0.001      Zero Imputation   
 67   LightGBM       NaN             0.0      Zero Imputation   
 68   LightGBM       NaN             100      Zero Imputation   
 69   LightGBM       NaN            None      Zero Imputation   
 70   LightGBM       NaN              31      Zero Imputation   
 71   LightGBM       NaN            None      Zero Imputation   
 72   LightGBM       NaN            None      Zero Imputation   
 73   LightGBM       NaN             0.0      Zero Imputation   
 74   LightGBM       NaN             0.0      Zero Imputation   
 75   LightGBM       NaN             1.0      Zero Imputation   
 76   LightGBM       NaN          200000      Zero Imputation   
 77   LightGBM       NaN               0      Zero Imputation   
 78   LightGBM  0.880927             NaN      Zero Imputation   
 79   LightGBM  0.127907             NaN      Zero Imputation   
 80   LightGBM  0.224490             NaN      Zero Imputation   
 81   LightGBM  0.162963             NaN      Zero Imputation   
 82   LightGBM  0.673694             NaN      Zero Imputation   
 83   LightGBM  0.276706             NaN      Zero Imputation   
 84   LightGBM  0.347388             NaN      Zero Imputation   
 85   LightGBM       NaN            gbdt      Zero Imputation   
 86   LightGBM       NaN            None      Zero Imputation   
 87   LightGBM       NaN             1.0      Zero Imputation   
 88   LightGBM       NaN           split      Zero Imputation   
 89   LightGBM       NaN             0.1      Zero Imputation   
 90   LightGBM       NaN              -1      Zero Imputation   
 91   LightGBM       NaN              20      Zero Imputation   
 92   LightGBM       NaN           0.001      Zero Imputation   
 93   LightGBM       NaN             0.0      Zero Imputation   
 94   LightGBM       NaN             100      Zero Imputation   
 95   LightGBM       NaN            None      Zero Imputation   
 96   LightGBM       NaN              31      Zero Imputation   
 97   LightGBM       NaN            None      Zero Imputation   
 98   LightGBM       NaN            None      Zero Imputation   
 99   LightGBM       NaN             0.0      Zero Imputation   
 100  LightGBM       NaN             0.0      Zero Imputation   
 101  LightGBM       NaN             1.0      Zero Imputation   
 102  LightGBM       NaN          200000      Zero Imputation   
 103  LightGBM       NaN               0      Zero Imputation   
 104  LightGBM  0.885406             NaN      Zero Imputation   
 105  LightGBM  0.136213             NaN      Zero Imputation   
 106  LightGBM  0.189815             NaN      Zero Imputation   
 107  LightGBM  0.158607             NaN      Zero Imputation   
 108  LightGBM  0.696215             NaN      Zero Imputation   
 109  LightGBM  0.294015             NaN      Zero Imputation   
 110  LightGBM  0.392430             NaN      Zero Imputation   
 111  LightGBM       NaN            gbdt      Zero Imputation   
 112  LightGBM       NaN            None      Zero Imputation   
 113  LightGBM       NaN             1.0      Zero Imputation   
 114  LightGBM       NaN           split      Zero Imputation   
 115  LightGBM       NaN             0.1      Zero Imputation   
 116  LightGBM       NaN              -1      Zero Imputation   
 117  LightGBM       NaN              20      Zero Imputation   
 118  LightGBM       NaN           0.001      Zero Imputation   
 119  LightGBM       NaN             0.0      Zero Imputation   
 120  LightGBM       NaN             100      Zero Imputation   
 121  LightGBM       NaN            None      Zero Imputation   
 122  LightGBM       NaN              31      Zero Imputation   
 123  LightGBM       NaN            None      Zero Imputation   
 124  LightGBM       NaN            None      Zero Imputation   
 125  LightGBM       NaN             0.0      Zero Imputation   
 126  LightGBM       NaN             0.0      Zero Imputation   
 127  LightGBM       NaN             1.0      Zero Imputation   
 128  LightGBM       NaN          200000      Zero Imputation   
 129  LightGBM       NaN               0      Zero Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 1    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 2    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 3    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 4    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 5    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 6    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 7    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 8    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 9    LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 10   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 11   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 12   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 13   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 14   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 15   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 16   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 17   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 18   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 19   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 20   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 21   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 22   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 23   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 24   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 25   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 26   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 27   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 28   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 29   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 30   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 31   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 32   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 33   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 34   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 35   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 36   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 37   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 38   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 39   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 40   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 41   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 42   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 43   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 44   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 45   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 46   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 47   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 48   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 49   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 50   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 51   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 52   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 53   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 54   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 55   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 56   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 57   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 58   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 59   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 60   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 61   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 62   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 63   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 64   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 65   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 66   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 67   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 68   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 69   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 70   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 71   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 72   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 73   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 74   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 75   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 76   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 77   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 78   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 79   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 80   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 81   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 82   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 83   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 84   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 85   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 86   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 87   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 88   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 89   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 90   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 91   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 92   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 93   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 94   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 95   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 96   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 97   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 98   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 99   LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 100  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 101  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 102  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 103  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 104  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 105  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 106  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 107  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 108  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 109  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 110  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 111  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 112  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 113  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 114  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 115  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 116  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 117  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 118  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 119  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 120  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 121  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 122  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 123  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 124  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 125  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 126  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 127  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 128  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  
 129  LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.886753             NaN      Mean Imputation   
 1    LightGBM  0.146520             NaN      Mean Imputation   
 2    LightGBM  0.168776             NaN      Mean Imputation   
 3    LightGBM  0.156863             NaN      Mean Imputation   
 4    LightGBM  0.669609             NaN      Mean Imputation   
 5    LightGBM  0.249951             NaN      Mean Imputation   
 6    LightGBM  0.339218             NaN      Mean Imputation   
 7    LightGBM       NaN            gbdt      Mean Imputation   
 8    LightGBM       NaN            None      Mean Imputation   
 9    LightGBM       NaN             1.0      Mean Imputation   
 10   LightGBM       NaN           split      Mean Imputation   
 11   LightGBM       NaN             0.1      Mean Imputation   
 12   LightGBM       NaN              -1      Mean Imputation   
 13   LightGBM       NaN              20      Mean Imputation   
 14   LightGBM       NaN           0.001      Mean Imputation   
 15   LightGBM       NaN             0.0      Mean Imputation   
 16   LightGBM       NaN             100      Mean Imputation   
 17   LightGBM       NaN            None      Mean Imputation   
 18   LightGBM       NaN              31      Mean Imputation   
 19   LightGBM       NaN            None      Mean Imputation   
 20   LightGBM       NaN            None      Mean Imputation   
 21   LightGBM       NaN             0.0      Mean Imputation   
 22   LightGBM       NaN             0.0      Mean Imputation   
 23   LightGBM       NaN             1.0      Mean Imputation   
 24   LightGBM       NaN          200000      Mean Imputation   
 25   LightGBM       NaN               0      Mean Imputation   
 26   LightGBM  0.893073             NaN      Mean Imputation   
 27   LightGBM  0.119497             NaN      Mean Imputation   
 28   LightGBM  0.231707             NaN      Mean Imputation   
 29   LightGBM  0.157676             NaN      Mean Imputation   
 30   LightGBM  0.695066             NaN      Mean Imputation   
 31   LightGBM  0.326407             NaN      Mean Imputation   
 32   LightGBM  0.390131             NaN      Mean Imputation   
 33   LightGBM       NaN            gbdt      Mean Imputation   
 34   LightGBM       NaN            None      Mean Imputation   
 35   LightGBM       NaN             1.0      Mean Imputation   
 36   LightGBM       NaN           split      Mean Imputation   
 37   LightGBM       NaN             0.1      Mean Imputation   
 38   LightGBM       NaN              -1      Mean Imputation   
 39   LightGBM       NaN              20      Mean Imputation   
 40   LightGBM       NaN           0.001      Mean Imputation   
 41   LightGBM       NaN             0.0      Mean Imputation   
 42   LightGBM       NaN             100      Mean Imputation   
 43   LightGBM       NaN            None      Mean Imputation   
 44   LightGBM       NaN              31      Mean Imputation   
 45   LightGBM       NaN            None      Mean Imputation   
 46   LightGBM       NaN            None      Mean Imputation   
 47   LightGBM       NaN             0.0      Mean Imputation   
 48   LightGBM       NaN             0.0      Mean Imputation   
 49   LightGBM       NaN             1.0      Mean Imputation   
 50   LightGBM       NaN          200000      Mean Imputation   
 51   LightGBM       NaN               0      Mean Imputation   
 52   LightGBM  0.885699             NaN      Mean Imputation   
 53   LightGBM  0.120000             NaN      Mean Imputation   
 54   LightGBM  0.208556             NaN      Mean Imputation   
 55   LightGBM  0.152344             NaN      Mean Imputation   
 56   LightGBM  0.665173             NaN      Mean Imputation   
 57   LightGBM  0.258015             NaN      Mean Imputation   
 58   LightGBM  0.330346             NaN      Mean Imputation   
 59   LightGBM       NaN            gbdt      Mean Imputation   
 60   LightGBM       NaN            None      Mean Imputation   
 61   LightGBM       NaN             1.0      Mean Imputation   
 62   LightGBM       NaN           split      Mean Imputation   
 63   LightGBM       NaN             0.1      Mean Imputation   
 64   LightGBM       NaN              -1      Mean Imputation   
 65   LightGBM       NaN              20      Mean Imputation   
 66   LightGBM       NaN           0.001      Mean Imputation   
 67   LightGBM       NaN             0.0      Mean Imputation   
 68   LightGBM       NaN             100      Mean Imputation   
 69   LightGBM       NaN            None      Mean Imputation   
 70   LightGBM       NaN              31      Mean Imputation   
 71   LightGBM       NaN            None      Mean Imputation   
 72   LightGBM       NaN            None      Mean Imputation   
 73   LightGBM       NaN             0.0      Mean Imputation   
 74   LightGBM       NaN             0.0      Mean Imputation   
 75   LightGBM       NaN             1.0      Mean Imputation   
 76   LightGBM       NaN          200000      Mean Imputation   
 77   LightGBM       NaN               0      Mean Imputation   
 78   LightGBM  0.896733             NaN      Mean Imputation   
 79   LightGBM  0.114173             NaN      Mean Imputation   
 80   LightGBM  0.147959             NaN      Mean Imputation   
 81   LightGBM  0.128889             NaN      Mean Imputation   
 82   LightGBM  0.669229             NaN      Mean Imputation   
 83   LightGBM  0.274785             NaN      Mean Imputation   
 84   LightGBM  0.338458             NaN      Mean Imputation   
 85   LightGBM       NaN            gbdt      Mean Imputation   
 86   LightGBM       NaN            None      Mean Imputation   
 87   LightGBM       NaN             1.0      Mean Imputation   
 88   LightGBM       NaN           split      Mean Imputation   
 89   LightGBM       NaN             0.1      Mean Imputation   
 90   LightGBM       NaN              -1      Mean Imputation   
 91   LightGBM       NaN              20      Mean Imputation   
 92   LightGBM       NaN           0.001      Mean Imputation   
 93   LightGBM       NaN             0.0      Mean Imputation   
 94   LightGBM       NaN             100      Mean Imputation   
 95   LightGBM       NaN            None      Mean Imputation   
 96   LightGBM       NaN              31      Mean Imputation   
 97   LightGBM       NaN            None      Mean Imputation   
 98   LightGBM       NaN            None      Mean Imputation   
 99   LightGBM       NaN             0.0      Mean Imputation   
 100  LightGBM       NaN             0.0      Mean Imputation   
 101  LightGBM       NaN             1.0      Mean Imputation   
 102  LightGBM       NaN          200000      Mean Imputation   
 103  LightGBM       NaN               0      Mean Imputation   
 104  LightGBM  0.889884             NaN      Mean Imputation   
 105  LightGBM  0.123134             NaN      Mean Imputation   
 106  LightGBM  0.152778             NaN      Mean Imputation   
 107  LightGBM  0.136364             NaN      Mean Imputation   
 108  LightGBM  0.690170             NaN      Mean Imputation   
 109  LightGBM  0.297134             NaN      Mean Imputation   
 110  LightGBM  0.380341             NaN      Mean Imputation   
 111  LightGBM       NaN            gbdt      Mean Imputation   
 112  LightGBM       NaN            None      Mean Imputation   
 113  LightGBM       NaN             1.0      Mean Imputation   
 114  LightGBM       NaN           split      Mean Imputation   
 115  LightGBM       NaN             0.1      Mean Imputation   
 116  LightGBM       NaN              -1      Mean Imputation   
 117  LightGBM       NaN              20      Mean Imputation   
 118  LightGBM       NaN           0.001      Mean Imputation   
 119  LightGBM       NaN             0.0      Mean Imputation   
 120  LightGBM       NaN             100      Mean Imputation   
 121  LightGBM       NaN            None      Mean Imputation   
 122  LightGBM       NaN              31      Mean Imputation   
 123  LightGBM       NaN            None      Mean Imputation   
 124  LightGBM       NaN            None      Mean Imputation   
 125  LightGBM       NaN             0.0      Mean Imputation   
 126  LightGBM       NaN             0.0      Mean Imputation   
 127  LightGBM       NaN             1.0      Mean Imputation   
 128  LightGBM       NaN          200000      Mean Imputation   
 129  LightGBM       NaN               0      Mean Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 1    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 2    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 3    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 4    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 5    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 6    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 7    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 8    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 9    LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 10   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 11   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 12   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 13   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 14   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 15   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 16   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 17   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 18   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 19   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 20   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 21   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 22   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 23   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 24   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 25   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 26   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 27   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 28   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 29   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 30   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 31   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 32   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 33   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 34   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 35   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 36   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 37   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 38   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 39   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 40   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 41   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 42   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 43   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 44   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 45   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 46   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 47   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 48   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 49   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 50   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 51   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 52   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 53   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 54   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 55   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 56   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 57   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 58   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 59   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 60   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 61   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 62   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 63   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 64   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 65   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 66   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 67   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 68   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 69   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 70   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 71   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 72   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 73   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 74   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 75   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 76   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 77   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 78   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 79   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 80   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 81   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 82   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 83   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 84   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 85   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 86   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 87   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 88   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 89   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 90   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 91   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 92   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 93   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 94   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 95   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 96   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 97   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 98   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 99   LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 100  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 101  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 102  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 103  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 104  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 105  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 106  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 107  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 108  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 109  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 110  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 111  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 112  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 113  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 114  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 115  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 116  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 117  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 118  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 119  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 120  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 121  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 122  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 123  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 124  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 125  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 126  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 127  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 128  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  
 129  LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.877798             NaN    Median Imputation   
 1    LightGBM  0.130293             NaN    Median Imputation   
 2    LightGBM  0.168776             NaN    Median Imputation   
 3    LightGBM  0.147059             NaN    Median Imputation   
 4    LightGBM  0.673820             NaN    Median Imputation   
 5    LightGBM  0.269272             NaN    Median Imputation   
 6    LightGBM  0.347640             NaN    Median Imputation   
 7    LightGBM       NaN            gbdt    Median Imputation   
 8    LightGBM       NaN            None    Median Imputation   
 9    LightGBM       NaN             1.0    Median Imputation   
 10   LightGBM       NaN           split    Median Imputation   
 11   LightGBM       NaN             0.1    Median Imputation   
 12   LightGBM       NaN              -1    Median Imputation   
 13   LightGBM       NaN              20    Median Imputation   
 14   LightGBM       NaN           0.001    Median Imputation   
 15   LightGBM       NaN             0.0    Median Imputation   
 16   LightGBM       NaN             100    Median Imputation   
 17   LightGBM       NaN            None    Median Imputation   
 18   LightGBM       NaN              31    Median Imputation   
 19   LightGBM       NaN            None    Median Imputation   
 20   LightGBM       NaN            None    Median Imputation   
 21   LightGBM       NaN             0.0    Median Imputation   
 22   LightGBM       NaN             0.0    Median Imputation   
 23   LightGBM       NaN             1.0    Median Imputation   
 24   LightGBM       NaN          200000    Median Imputation   
 25   LightGBM       NaN               0    Median Imputation   
 26   LightGBM  0.894390             NaN    Median Imputation   
 27   LightGBM  0.106312             NaN    Median Imputation   
 28   LightGBM  0.195122             NaN    Median Imputation   
 29   LightGBM  0.137634             NaN    Median Imputation   
 30   LightGBM  0.696201             NaN    Median Imputation   
 31   LightGBM  0.313278             NaN    Median Imputation   
 32   LightGBM  0.392402             NaN    Median Imputation   
 33   LightGBM       NaN            gbdt    Median Imputation   
 34   LightGBM       NaN            None    Median Imputation   
 35   LightGBM       NaN             1.0    Median Imputation   
 36   LightGBM       NaN           split    Median Imputation   
 37   LightGBM       NaN             0.1    Median Imputation   
 38   LightGBM       NaN              -1    Median Imputation   
 39   LightGBM       NaN              20    Median Imputation   
 40   LightGBM       NaN           0.001    Median Imputation   
 41   LightGBM       NaN             0.0    Median Imputation   
 42   LightGBM       NaN             100    Median Imputation   
 43   LightGBM       NaN            None    Median Imputation   
 44   LightGBM       NaN              31    Median Imputation   
 45   LightGBM       NaN            None    Median Imputation   
 46   LightGBM       NaN            None    Median Imputation   
 47   LightGBM       NaN             0.0    Median Imputation   
 48   LightGBM       NaN             0.0    Median Imputation   
 49   LightGBM       NaN             1.0    Median Imputation   
 50   LightGBM       NaN          200000    Median Imputation   
 51   LightGBM       NaN               0    Median Imputation   
 52   LightGBM  0.892283             NaN    Median Imputation   
 53   LightGBM  0.114583             NaN    Median Imputation   
 54   LightGBM  0.176471             NaN    Median Imputation   
 55   LightGBM  0.138947             NaN    Median Imputation   
 56   LightGBM  0.668333             NaN    Median Imputation   
 57   LightGBM  0.267837             NaN    Median Imputation   
 58   LightGBM  0.336666             NaN    Median Imputation   
 59   LightGBM       NaN            gbdt    Median Imputation   
 60   LightGBM       NaN            None    Median Imputation   
 61   LightGBM       NaN             1.0    Median Imputation   
 62   LightGBM       NaN           split    Median Imputation   
 63   LightGBM       NaN             0.1    Median Imputation   
 64   LightGBM       NaN              -1    Median Imputation   
 65   LightGBM       NaN              20    Median Imputation   
 66   LightGBM       NaN           0.001    Median Imputation   
 67   LightGBM       NaN             0.0    Median Imputation   
 68   LightGBM       NaN             100    Median Imputation   
 69   LightGBM       NaN            None    Median Imputation   
 70   LightGBM       NaN              31    Median Imputation   
 71   LightGBM       NaN            None    Median Imputation   
 72   LightGBM       NaN            None    Median Imputation   
 73   LightGBM       NaN             0.0    Median Imputation   
 74   LightGBM       NaN             0.0    Median Imputation   
 75   LightGBM       NaN             1.0    Median Imputation   
 76   LightGBM       NaN          200000    Median Imputation   
 77   LightGBM       NaN               0    Median Imputation   
 78   LightGBM  0.890148             NaN    Median Imputation   
 79   LightGBM  0.132890             NaN    Median Imputation   
 80   LightGBM  0.204082             NaN    Median Imputation   
 81   LightGBM  0.160966             NaN    Median Imputation   
 82   LightGBM  0.659871             NaN    Median Imputation   
 83   LightGBM  0.239592             NaN    Median Imputation   
 84   LightGBM  0.319742             NaN    Median Imputation   
 85   LightGBM       NaN            gbdt    Median Imputation   
 86   LightGBM       NaN            None    Median Imputation   
 87   LightGBM       NaN             1.0    Median Imputation   
 88   LightGBM       NaN           split    Median Imputation   
 89   LightGBM       NaN             0.1    Median Imputation   
 90   LightGBM       NaN              -1    Median Imputation   
 91   LightGBM       NaN              20    Median Imputation   
 92   LightGBM       NaN           0.001    Median Imputation   
 93   LightGBM       NaN             0.0    Median Imputation   
 94   LightGBM       NaN             100    Median Imputation   
 95   LightGBM       NaN            None    Median Imputation   
 96   LightGBM       NaN              31    Median Imputation   
 97   LightGBM       NaN            None    Median Imputation   
 98   LightGBM       NaN            None    Median Imputation   
 99   LightGBM       NaN             0.0    Median Imputation   
 100  LightGBM       NaN             0.0    Median Imputation   
 101  LightGBM       NaN             1.0    Median Imputation   
 102  LightGBM       NaN          200000    Median Imputation   
 103  LightGBM       NaN               0    Median Imputation   
 104  LightGBM  0.893309             NaN    Median Imputation   
 105  LightGBM  0.153846             NaN    Median Imputation   
 106  LightGBM  0.194444             NaN    Median Imputation   
 107  LightGBM  0.171779             NaN    Median Imputation   
 108  LightGBM  0.688048             NaN    Median Imputation   
 109  LightGBM  0.305783             NaN    Median Imputation   
 110  LightGBM  0.376097             NaN    Median Imputation   
 111  LightGBM       NaN            gbdt    Median Imputation   
 112  LightGBM       NaN            None    Median Imputation   
 113  LightGBM       NaN             1.0    Median Imputation   
 114  LightGBM       NaN           split    Median Imputation   
 115  LightGBM       NaN             0.1    Median Imputation   
 116  LightGBM       NaN              -1    Median Imputation   
 117  LightGBM       NaN              20    Median Imputation   
 118  LightGBM       NaN           0.001    Median Imputation   
 119  LightGBM       NaN             0.0    Median Imputation   
 120  LightGBM       NaN             100    Median Imputation   
 121  LightGBM       NaN            None    Median Imputation   
 122  LightGBM       NaN              31    Median Imputation   
 123  LightGBM       NaN            None    Median Imputation   
 124  LightGBM       NaN            None    Median Imputation   
 125  LightGBM       NaN             0.0    Median Imputation   
 126  LightGBM       NaN             0.0    Median Imputation   
 127  LightGBM       NaN             1.0    Median Imputation   
 128  LightGBM       NaN          200000    Median Imputation   
 129  LightGBM       NaN               0    Median Imputation   
 
       Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTEENN)   
 1    Hybrid Sampling (SMOTEENN)   
 2    Hybrid Sampling (SMOTEENN)   
 3    Hybrid Sampling (SMOTEENN)   
 4    Hybrid Sampling (SMOTEENN)   
 5    Hybrid Sampling (SMOTEENN)   
 6    Hybrid Sampling (SMOTEENN)   
 7    Hybrid Sampling (SMOTEENN)   
 8    Hybrid Sampling (SMOTEENN)   
 9    Hybrid Sampling (SMOTEENN)   
 10   Hybrid Sampling (SMOTEENN)   
 11   Hybrid Sampling (SMOTEENN)   
 12   Hybrid Sampling (SMOTEENN)   
 13   Hybrid Sampling (SMOTEENN)   
 14   Hybrid Sampling (SMOTEENN)   
 15   Hybrid Sampling (SMOTEENN)   
 16   Hybrid Sampling (SMOTEENN)   
 17   Hybrid Sampling (SMOTEENN)   
 18   Hybrid Sampling (SMOTEENN)   
 19   Hybrid Sampling (SMOTEENN)   
 20   Hybrid Sampling (SMOTEENN)   
 21   Hybrid Sampling (SMOTEENN)   
 22   Hybrid Sampling (SMOTEENN)   
 23   Hybrid Sampling (SMOTEENN)   
 24   Hybrid Sampling (SMOTEENN)   
 25   Hybrid Sampling (SMOTEENN)   
 26   Hybrid Sampling (SMOTEENN)   
 27   Hybrid Sampling (SMOTEENN)   
 28   Hybrid Sampling (SMOTEENN)   
 29   Hybrid Sampling (SMOTEENN)   
 30   Hybrid Sampling (SMOTEENN)   
 31   Hybrid Sampling (SMOTEENN)   
 32   Hybrid Sampling (SMOTEENN)   
 33   Hybrid Sampling (SMOTEENN)   
 34   Hybrid Sampling (SMOTEENN)   
 35   Hybrid Sampling (SMOTEENN)   
 36   Hybrid Sampling (SMOTEENN)   
 37   Hybrid Sampling (SMOTEENN)   
 38   Hybrid Sampling (SMOTEENN)   
 39   Hybrid Sampling (SMOTEENN)   
 40   Hybrid Sampling (SMOTEENN)   
 41   Hybrid Sampling (SMOTEENN)   
 42   Hybrid Sampling (SMOTEENN)   
 43   Hybrid Sampling (SMOTEENN)   
 44   Hybrid Sampling (SMOTEENN)   
 45   Hybrid Sampling (SMOTEENN)   
 46   Hybrid Sampling (SMOTEENN)   
 47   Hybrid Sampling (SMOTEENN)   
 48   Hybrid Sampling (SMOTEENN)   
 49   Hybrid Sampling (SMOTEENN)   
 50   Hybrid Sampling (SMOTEENN)   
 51   Hybrid Sampling (SMOTEENN)   
 52   Hybrid Sampling (SMOTEENN)   
 53   Hybrid Sampling (SMOTEENN)   
 54   Hybrid Sampling (SMOTEENN)   
 55   Hybrid Sampling (SMOTEENN)   
 56   Hybrid Sampling (SMOTEENN)   
 57   Hybrid Sampling (SMOTEENN)   
 58   Hybrid Sampling (SMOTEENN)   
 59   Hybrid Sampling (SMOTEENN)   
 60   Hybrid Sampling (SMOTEENN)   
 61   Hybrid Sampling (SMOTEENN)   
 62   Hybrid Sampling (SMOTEENN)   
 63   Hybrid Sampling (SMOTEENN)   
 64   Hybrid Sampling (SMOTEENN)   
 65   Hybrid Sampling (SMOTEENN)   
 66   Hybrid Sampling (SMOTEENN)   
 67   Hybrid Sampling (SMOTEENN)   
 68   Hybrid Sampling (SMOTEENN)   
 69   Hybrid Sampling (SMOTEENN)   
 70   Hybrid Sampling (SMOTEENN)   
 71   Hybrid Sampling (SMOTEENN)   
 72   Hybrid Sampling (SMOTEENN)   
 73   Hybrid Sampling (SMOTEENN)   
 74   Hybrid Sampling (SMOTEENN)   
 75   Hybrid Sampling (SMOTEENN)   
 76   Hybrid Sampling (SMOTEENN)   
 77   Hybrid Sampling (SMOTEENN)   
 78   Hybrid Sampling (SMOTEENN)   
 79   Hybrid Sampling (SMOTEENN)   
 80   Hybrid Sampling (SMOTEENN)   
 81   Hybrid Sampling (SMOTEENN)   
 82   Hybrid Sampling (SMOTEENN)   
 83   Hybrid Sampling (SMOTEENN)   
 84   Hybrid Sampling (SMOTEENN)   
 85   Hybrid Sampling (SMOTEENN)   
 86   Hybrid Sampling (SMOTEENN)   
 87   Hybrid Sampling (SMOTEENN)   
 88   Hybrid Sampling (SMOTEENN)   
 89   Hybrid Sampling (SMOTEENN)   
 90   Hybrid Sampling (SMOTEENN)   
 91   Hybrid Sampling (SMOTEENN)   
 92   Hybrid Sampling (SMOTEENN)   
 93   Hybrid Sampling (SMOTEENN)   
 94   Hybrid Sampling (SMOTEENN)   
 95   Hybrid Sampling (SMOTEENN)   
 96   Hybrid Sampling (SMOTEENN)   
 97   Hybrid Sampling (SMOTEENN)   
 98   Hybrid Sampling (SMOTEENN)   
 99   Hybrid Sampling (SMOTEENN)   
 100  Hybrid Sampling (SMOTEENN)   
 101  Hybrid Sampling (SMOTEENN)   
 102  Hybrid Sampling (SMOTEENN)   
 103  Hybrid Sampling (SMOTEENN)   
 104  Hybrid Sampling (SMOTEENN)   
 105  Hybrid Sampling (SMOTEENN)   
 106  Hybrid Sampling (SMOTEENN)   
 107  Hybrid Sampling (SMOTEENN)   
 108  Hybrid Sampling (SMOTEENN)   
 109  Hybrid Sampling (SMOTEENN)   
 110  Hybrid Sampling (SMOTEENN)   
 111  Hybrid Sampling (SMOTEENN)   
 112  Hybrid Sampling (SMOTEENN)   
 113  Hybrid Sampling (SMOTEENN)   
 114  Hybrid Sampling (SMOTEENN)   
 115  Hybrid Sampling (SMOTEENN)   
 116  Hybrid Sampling (SMOTEENN)   
 117  Hybrid Sampling (SMOTEENN)   
 118  Hybrid Sampling (SMOTEENN)   
 119  Hybrid Sampling (SMOTEENN)   
 120  Hybrid Sampling (SMOTEENN)   
 121  Hybrid Sampling (SMOTEENN)   
 122  Hybrid Sampling (SMOTEENN)   
 123  Hybrid Sampling (SMOTEENN)   
 124  Hybrid Sampling (SMOTEENN)   
 125  Hybrid Sampling (SMOTEENN)   
 126  Hybrid Sampling (SMOTEENN)   
 127  Hybrid Sampling (SMOTEENN)   
 128  Hybrid Sampling (SMOTEENN)   
 129  Hybrid Sampling (SMOTEENN)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 1    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 2    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 3    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 4    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 5    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 6    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 7    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 8    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 9    LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 10   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 11   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 12   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 13   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 14   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 15   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 16   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 17   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 18   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 19   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 20   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 21   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 22   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 23   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 24   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 25   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 26   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 27   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 28   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 29   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 30   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 31   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 32   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 33   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 34   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 35   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 36   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 37   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 38   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 39   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 40   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 41   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 42   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 43   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 44   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 45   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 46   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 47   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 48   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 49   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 50   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 51   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 52   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 53   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 54   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 55   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 56   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 57   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 58   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 59   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 60   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 61   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 62   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 63   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 64   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 65   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 66   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 67   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 68   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 69   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 70   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 71   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 72   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 73   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 74   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 75   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 76   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 77   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 78   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 79   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 80   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 81   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 82   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 83   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 84   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 85   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 86   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 87   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 88   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 89   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 90   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 91   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 92   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 93   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 94   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 95   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 96   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 97   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 98   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 99   LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 100  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 101  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 102  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 103  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 104  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 105  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 106  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 107  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 108  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 109  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 110  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 111  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 112  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 113  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 114  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 115  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 116  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 117  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 118  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 119  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 120  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 121  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 122  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 123  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 124  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 125  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 126  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 127  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 128  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  
 129  LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.878062             NaN      Zero Imputation   
 1    LightGBM  0.133117             NaN      Zero Imputation   
 2    LightGBM  0.172996             NaN      Zero Imputation   
 3    LightGBM  0.150459             NaN      Zero Imputation   
 4    LightGBM  0.669074             NaN      Zero Imputation   
 5    LightGBM  0.293829             NaN      Zero Imputation   
 6    LightGBM  0.338148             NaN      Zero Imputation   
 7    LightGBM       NaN            gbdt      Zero Imputation   
 8    LightGBM       NaN            None      Zero Imputation   
 9    LightGBM       NaN             1.0      Zero Imputation   
 10   LightGBM       NaN           split      Zero Imputation   
 11   LightGBM       NaN             0.1      Zero Imputation   
 12   LightGBM       NaN              -1      Zero Imputation   
 13   LightGBM       NaN              20      Zero Imputation   
 14   LightGBM       NaN           0.001      Zero Imputation   
 15   LightGBM       NaN             0.0      Zero Imputation   
 16   LightGBM       NaN             100      Zero Imputation   
 17   LightGBM       NaN            None      Zero Imputation   
 18   LightGBM       NaN              31      Zero Imputation   
 19   LightGBM       NaN            None      Zero Imputation   
 20   LightGBM       NaN            None      Zero Imputation   
 21   LightGBM       NaN             0.0      Zero Imputation   
 22   LightGBM       NaN             0.0      Zero Imputation   
 23   LightGBM       NaN             1.0      Zero Imputation   
 24   LightGBM       NaN          200000      Zero Imputation   
 25   LightGBM       NaN               0      Zero Imputation   
 26   LightGBM  0.900711             NaN      Zero Imputation   
 27   LightGBM  0.120996             NaN      Zero Imputation   
 28   LightGBM  0.207317             NaN      Zero Imputation   
 29   LightGBM  0.152809             NaN      Zero Imputation   
 30   LightGBM  0.698845             NaN      Zero Imputation   
 31   LightGBM  0.317066             NaN      Zero Imputation   
 32   LightGBM  0.397689             NaN      Zero Imputation   
 33   LightGBM       NaN            gbdt      Zero Imputation   
 34   LightGBM       NaN            None      Zero Imputation   
 35   LightGBM       NaN             1.0      Zero Imputation   
 36   LightGBM       NaN           split      Zero Imputation   
 37   LightGBM       NaN             0.1      Zero Imputation   
 38   LightGBM       NaN              -1      Zero Imputation   
 39   LightGBM       NaN              20      Zero Imputation   
 40   LightGBM       NaN           0.001      Zero Imputation   
 41   LightGBM       NaN             0.0      Zero Imputation   
 42   LightGBM       NaN             100      Zero Imputation   
 43   LightGBM       NaN            None      Zero Imputation   
 44   LightGBM       NaN              31      Zero Imputation   
 45   LightGBM       NaN            None      Zero Imputation   
 46   LightGBM       NaN            None      Zero Imputation   
 47   LightGBM       NaN             0.0      Zero Imputation   
 48   LightGBM       NaN             0.0      Zero Imputation   
 49   LightGBM       NaN             1.0      Zero Imputation   
 50   LightGBM       NaN          200000      Zero Imputation   
 51   LightGBM       NaN               0      Zero Imputation   
 52   LightGBM  0.894917             NaN      Zero Imputation   
 53   LightGBM  0.131944             NaN      Zero Imputation   
 54   LightGBM  0.203209             NaN      Zero Imputation   
 55   LightGBM  0.160000             NaN      Zero Imputation   
 56   LightGBM  0.686604             NaN      Zero Imputation   
 57   LightGBM  0.286658             NaN      Zero Imputation   
 58   LightGBM  0.373209             NaN      Zero Imputation   
 59   LightGBM       NaN            gbdt      Zero Imputation   
 60   LightGBM       NaN            None      Zero Imputation   
 61   LightGBM       NaN             1.0      Zero Imputation   
 62   LightGBM       NaN           split      Zero Imputation   
 63   LightGBM       NaN             0.1      Zero Imputation   
 64   LightGBM       NaN              -1      Zero Imputation   
 65   LightGBM       NaN              20      Zero Imputation   
 66   LightGBM       NaN           0.001      Zero Imputation   
 67   LightGBM       NaN             0.0      Zero Imputation   
 68   LightGBM       NaN             100      Zero Imputation   
 69   LightGBM       NaN            None      Zero Imputation   
 70   LightGBM       NaN              31      Zero Imputation   
 71   LightGBM       NaN            None      Zero Imputation   
 72   LightGBM       NaN            None      Zero Imputation   
 73   LightGBM       NaN             0.0      Zero Imputation   
 74   LightGBM       NaN             0.0      Zero Imputation   
 75   LightGBM       NaN             1.0      Zero Imputation   
 76   LightGBM       NaN          200000      Zero Imputation   
 77   LightGBM       NaN               0      Zero Imputation   
 78   LightGBM  0.890411             NaN      Zero Imputation   
 79   LightGBM  0.128378             NaN      Zero Imputation   
 80   LightGBM  0.193878             NaN      Zero Imputation   
 81   LightGBM  0.154472             NaN      Zero Imputation   
 82   LightGBM  0.679689             NaN      Zero Imputation   
 83   LightGBM  0.269558             NaN      Zero Imputation   
 84   LightGBM  0.359378             NaN      Zero Imputation   
 85   LightGBM       NaN            gbdt      Zero Imputation   
 86   LightGBM       NaN            None      Zero Imputation   
 87   LightGBM       NaN             1.0      Zero Imputation   
 88   LightGBM       NaN           split      Zero Imputation   
 89   LightGBM       NaN             0.1      Zero Imputation   
 90   LightGBM       NaN              -1      Zero Imputation   
 91   LightGBM       NaN              20      Zero Imputation   
 92   LightGBM       NaN           0.001      Zero Imputation   
 93   LightGBM       NaN             0.0      Zero Imputation   
 94   LightGBM       NaN             100      Zero Imputation   
 95   LightGBM       NaN            None      Zero Imputation   
 96   LightGBM       NaN              31      Zero Imputation   
 97   LightGBM       NaN            None      Zero Imputation   
 98   LightGBM       NaN            None      Zero Imputation   
 99   LightGBM       NaN             0.0      Zero Imputation   
 100  LightGBM       NaN             0.0      Zero Imputation   
 101  LightGBM       NaN             1.0      Zero Imputation   
 102  LightGBM       NaN          200000      Zero Imputation   
 103  LightGBM       NaN               0      Zero Imputation   
 104  LightGBM  0.890148             NaN      Zero Imputation   
 105  LightGBM  0.144876             NaN      Zero Imputation   
 106  LightGBM  0.189815             NaN      Zero Imputation   
 107  LightGBM  0.164329             NaN      Zero Imputation   
 108  LightGBM  0.701465             NaN      Zero Imputation   
 109  LightGBM  0.325719             NaN      Zero Imputation   
 110  LightGBM  0.402929             NaN      Zero Imputation   
 111  LightGBM       NaN            gbdt      Zero Imputation   
 112  LightGBM       NaN            None      Zero Imputation   
 113  LightGBM       NaN             1.0      Zero Imputation   
 114  LightGBM       NaN           split      Zero Imputation   
 115  LightGBM       NaN             0.1      Zero Imputation   
 116  LightGBM       NaN              -1      Zero Imputation   
 117  LightGBM       NaN              20      Zero Imputation   
 118  LightGBM       NaN           0.001      Zero Imputation   
 119  LightGBM       NaN             0.0      Zero Imputation   
 120  LightGBM       NaN             100      Zero Imputation   
 121  LightGBM       NaN            None      Zero Imputation   
 122  LightGBM       NaN              31      Zero Imputation   
 123  LightGBM       NaN            None      Zero Imputation   
 124  LightGBM       NaN            None      Zero Imputation   
 125  LightGBM       NaN             0.0      Zero Imputation   
 126  LightGBM       NaN             0.0      Zero Imputation   
 127  LightGBM       NaN             1.0      Zero Imputation   
 128  LightGBM       NaN          200000      Zero Imputation   
 129  LightGBM       NaN               0      Zero Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 1    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 2    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 3    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 4    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 5    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 6    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 7    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 8    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 9    LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 10   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 11   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 12   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 13   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 14   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 15   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 16   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 17   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 18   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 19   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 20   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 21   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 22   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 23   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 24   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 25   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 26   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 27   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 28   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 29   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 30   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 31   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 32   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 33   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 34   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 35   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 36   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 37   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 38   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 39   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 40   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 41   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 42   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 43   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 44   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 45   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 46   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 47   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 48   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 49   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 50   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 51   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 52   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 53   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 54   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 55   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 56   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 57   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 58   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 59   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 60   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 61   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 62   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 63   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 64   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 65   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 66   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 67   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 68   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 69   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 70   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 71   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 72   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 73   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 74   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 75   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 76   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 77   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 78   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 79   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 80   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 81   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 82   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 83   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 84   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 85   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 86   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 87   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 88   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 89   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 90   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 91   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 92   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 93   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 94   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 95   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 96   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 97   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 98   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 99   LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 100  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 101  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 102  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 103  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 104  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 105  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 106  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 107  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 108  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 109  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 110  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 111  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 112  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 113  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 114  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 115  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 116  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 117  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 118  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 119  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 120  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 121  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 122  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 123  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 124  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 125  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 126  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 127  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 128  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  
 129  LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.883066             NaN      Mean Imputation   
 1    LightGBM  0.144330             NaN      Mean Imputation   
 2    LightGBM  0.177215             NaN      Mean Imputation   
 3    LightGBM  0.159091             NaN      Mean Imputation   
 4    LightGBM  0.673649             NaN      Mean Imputation   
 5    LightGBM  0.279163             NaN      Mean Imputation   
 6    LightGBM  0.347299             NaN      Mean Imputation   
 7    LightGBM       NaN            gbdt      Mean Imputation   
 8    LightGBM       NaN            None      Mean Imputation   
 9    LightGBM       NaN             1.0      Mean Imputation   
 10   LightGBM       NaN           split      Mean Imputation   
 11   LightGBM       NaN             0.1      Mean Imputation   
 12   LightGBM       NaN              -1      Mean Imputation   
 13   LightGBM       NaN              20      Mean Imputation   
 14   LightGBM       NaN           0.001      Mean Imputation   
 15   LightGBM       NaN             0.0      Mean Imputation   
 16   LightGBM       NaN             100      Mean Imputation   
 17   LightGBM       NaN            None      Mean Imputation   
 18   LightGBM       NaN              31      Mean Imputation   
 19   LightGBM       NaN            None      Mean Imputation   
 20   LightGBM       NaN            None      Mean Imputation   
 21   LightGBM       NaN             0.0      Mean Imputation   
 22   LightGBM       NaN             0.0      Mean Imputation   
 23   LightGBM       NaN             1.0      Mean Imputation   
 24   LightGBM       NaN          200000      Mean Imputation   
 25   LightGBM       NaN               0      Mean Imputation   
 26   LightGBM  0.901765             NaN      Mean Imputation   
 27   LightGBM  0.114391             NaN      Mean Imputation   
 28   LightGBM  0.189024             NaN      Mean Imputation   
 29   LightGBM  0.142529             NaN      Mean Imputation   
 30   LightGBM  0.675542             NaN      Mean Imputation   
 31   LightGBM  0.270763             NaN      Mean Imputation   
 32   LightGBM  0.351084             NaN      Mean Imputation   
 33   LightGBM       NaN            gbdt      Mean Imputation   
 34   LightGBM       NaN            None      Mean Imputation   
 35   LightGBM       NaN             1.0      Mean Imputation   
 36   LightGBM       NaN           split      Mean Imputation   
 37   LightGBM       NaN             0.1      Mean Imputation   
 38   LightGBM       NaN              -1      Mean Imputation   
 39   LightGBM       NaN              20      Mean Imputation   
 40   LightGBM       NaN           0.001      Mean Imputation   
 41   LightGBM       NaN             0.0      Mean Imputation   
 42   LightGBM       NaN             100      Mean Imputation   
 43   LightGBM       NaN            None      Mean Imputation   
 44   LightGBM       NaN              31      Mean Imputation   
 45   LightGBM       NaN            None      Mean Imputation   
 46   LightGBM       NaN            None      Mean Imputation   
 47   LightGBM       NaN             0.0      Mean Imputation   
 48   LightGBM       NaN             0.0      Mean Imputation   
 49   LightGBM       NaN             1.0      Mean Imputation   
 50   LightGBM       NaN          200000      Mean Imputation   
 51   LightGBM       NaN               0      Mean Imputation   
 52   LightGBM  0.883329             NaN      Mean Imputation   
 53   LightGBM  0.127907             NaN      Mean Imputation   
 54   LightGBM  0.235294             NaN      Mean Imputation   
 55   LightGBM  0.165725             NaN      Mean Imputation   
 56   LightGBM  0.686167             NaN      Mean Imputation   
 57   LightGBM  0.310858             NaN      Mean Imputation   
 58   LightGBM  0.372333             NaN      Mean Imputation   
 59   LightGBM       NaN            gbdt      Mean Imputation   
 60   LightGBM       NaN            None      Mean Imputation   
 61   LightGBM       NaN             1.0      Mean Imputation   
 62   LightGBM       NaN           split      Mean Imputation   
 63   LightGBM       NaN             0.1      Mean Imputation   
 64   LightGBM       NaN              -1      Mean Imputation   
 65   LightGBM       NaN              20      Mean Imputation   
 66   LightGBM       NaN           0.001      Mean Imputation   
 67   LightGBM       NaN             0.0      Mean Imputation   
 68   LightGBM       NaN             100      Mean Imputation   
 69   LightGBM       NaN            None      Mean Imputation   
 70   LightGBM       NaN              31      Mean Imputation   
 71   LightGBM       NaN            None      Mean Imputation   
 72   LightGBM       NaN            None      Mean Imputation   
 73   LightGBM       NaN             0.0      Mean Imputation   
 74   LightGBM       NaN             0.0      Mean Imputation   
 75   LightGBM       NaN             1.0      Mean Imputation   
 76   LightGBM       NaN          200000      Mean Imputation   
 77   LightGBM       NaN               0      Mean Imputation   
 78   LightGBM  0.890148             NaN      Mean Imputation   
 79   LightGBM  0.122867             NaN      Mean Imputation   
 80   LightGBM  0.183673             NaN      Mean Imputation   
 81   LightGBM  0.147239             NaN      Mean Imputation   
 82   LightGBM  0.683311             NaN      Mean Imputation   
 83   LightGBM  0.310091             NaN      Mean Imputation   
 84   LightGBM  0.366621             NaN      Mean Imputation   
 85   LightGBM       NaN            gbdt      Mean Imputation   
 86   LightGBM       NaN            None      Mean Imputation   
 87   LightGBM       NaN             1.0      Mean Imputation   
 88   LightGBM       NaN           split      Mean Imputation   
 89   LightGBM       NaN             0.1      Mean Imputation   
 90   LightGBM       NaN              -1      Mean Imputation   
 91   LightGBM       NaN              20      Mean Imputation   
 92   LightGBM       NaN           0.001      Mean Imputation   
 93   LightGBM       NaN             0.0      Mean Imputation   
 94   LightGBM       NaN             100      Mean Imputation   
 95   LightGBM       NaN            None      Mean Imputation   
 96   LightGBM       NaN              31      Mean Imputation   
 97   LightGBM       NaN            None      Mean Imputation   
 98   LightGBM       NaN            None      Mean Imputation   
 99   LightGBM       NaN             0.0      Mean Imputation   
 100  LightGBM       NaN             0.0      Mean Imputation   
 101  LightGBM       NaN             1.0      Mean Imputation   
 102  LightGBM       NaN          200000      Mean Imputation   
 103  LightGBM       NaN               0      Mean Imputation   
 104  LightGBM  0.892518             NaN      Mean Imputation   
 105  LightGBM  0.154676             NaN      Mean Imputation   
 106  LightGBM  0.199074             NaN      Mean Imputation   
 107  LightGBM  0.174089             NaN      Mean Imputation   
 108  LightGBM  0.699898             NaN      Mean Imputation   
 109  LightGBM  0.331523             NaN      Mean Imputation   
 110  LightGBM  0.399797             NaN      Mean Imputation   
 111  LightGBM       NaN            gbdt      Mean Imputation   
 112  LightGBM       NaN            None      Mean Imputation   
 113  LightGBM       NaN             1.0      Mean Imputation   
 114  LightGBM       NaN           split      Mean Imputation   
 115  LightGBM       NaN             0.1      Mean Imputation   
 116  LightGBM       NaN              -1      Mean Imputation   
 117  LightGBM       NaN              20      Mean Imputation   
 118  LightGBM       NaN           0.001      Mean Imputation   
 119  LightGBM       NaN             0.0      Mean Imputation   
 120  LightGBM       NaN             100      Mean Imputation   
 121  LightGBM       NaN            None      Mean Imputation   
 122  LightGBM       NaN              31      Mean Imputation   
 123  LightGBM       NaN            None      Mean Imputation   
 124  LightGBM       NaN            None      Mean Imputation   
 125  LightGBM       NaN             0.0      Mean Imputation   
 126  LightGBM       NaN             0.0      Mean Imputation   
 127  LightGBM       NaN             1.0      Mean Imputation   
 128  LightGBM       NaN          200000      Mean Imputation   
 129  LightGBM       NaN               0      Mean Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 1    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 2    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 3    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 4    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 5    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 6    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 7    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 8    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 9    LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 10   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 11   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 12   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 13   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 14   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 15   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 16   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 17   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 18   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 19   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 20   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 21   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 22   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 23   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 24   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 25   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 26   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 27   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 28   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 29   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 30   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 31   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 32   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 33   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 34   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 35   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 36   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 37   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 38   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 39   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 40   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 41   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 42   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 43   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 44   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 45   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 46   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 47   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 48   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 49   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 50   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 51   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 52   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 53   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 54   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 55   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 56   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 57   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 58   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 59   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 60   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 61   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 62   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 63   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 64   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 65   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 66   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 67   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 68   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 69   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 70   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 71   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 72   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 73   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 74   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 75   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 76   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 77   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 78   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 79   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 80   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 81   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 82   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 83   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 84   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 85   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 86   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 87   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 88   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 89   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 90   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 91   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 92   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 93   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 94   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 95   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 96   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 97   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 98   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 99   LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 100  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 101  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 102  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 103  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 104  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 105  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 106  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 107  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 108  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 109  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 110  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 111  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 112  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 113  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 114  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 115  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 116  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 117  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 118  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 119  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 120  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 121  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 122  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 123  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 124  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 125  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 126  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 127  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 128  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  
 129  LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...  ,
     Algorithm   Metrics Hyperparameters Imputation Technique  \
 0    LightGBM  0.882012             NaN    Median Imputation   
 1    LightGBM  0.129825             NaN    Median Imputation   
 2    LightGBM  0.156118             NaN    Median Imputation   
 3    LightGBM  0.141762             NaN    Median Imputation   
 4    LightGBM  0.672112             NaN    Median Imputation   
 5    LightGBM  0.271770             NaN    Median Imputation   
 6    LightGBM  0.344224             NaN    Median Imputation   
 7    LightGBM       NaN            gbdt    Median Imputation   
 8    LightGBM       NaN            None    Median Imputation   
 9    LightGBM       NaN             1.0    Median Imputation   
 10   LightGBM       NaN           split    Median Imputation   
 11   LightGBM       NaN             0.1    Median Imputation   
 12   LightGBM       NaN              -1    Median Imputation   
 13   LightGBM       NaN              20    Median Imputation   
 14   LightGBM       NaN           0.001    Median Imputation   
 15   LightGBM       NaN             0.0    Median Imputation   
 16   LightGBM       NaN             100    Median Imputation   
 17   LightGBM       NaN            None    Median Imputation   
 18   LightGBM       NaN              31    Median Imputation   
 19   LightGBM       NaN            None    Median Imputation   
 20   LightGBM       NaN            None    Median Imputation   
 21   LightGBM       NaN             0.0    Median Imputation   
 22   LightGBM       NaN             0.0    Median Imputation   
 23   LightGBM       NaN             1.0    Median Imputation   
 24   LightGBM       NaN          200000    Median Imputation   
 25   LightGBM       NaN               0    Median Imputation   
 26   LightGBM  0.904398             NaN    Median Imputation   
 27   LightGBM  0.112840             NaN    Median Imputation   
 28   LightGBM  0.176829             NaN    Median Imputation   
 29   LightGBM  0.137767             NaN    Median Imputation   
 30   LightGBM  0.677074             NaN    Median Imputation   
 31   LightGBM  0.279128             NaN    Median Imputation   
 32   LightGBM  0.354149             NaN    Median Imputation   
 33   LightGBM       NaN            gbdt    Median Imputation   
 34   LightGBM       NaN            None    Median Imputation   
 35   LightGBM       NaN             1.0    Median Imputation   
 36   LightGBM       NaN           split    Median Imputation   
 37   LightGBM       NaN             0.1    Median Imputation   
 38   LightGBM       NaN              -1    Median Imputation   
 39   LightGBM       NaN              20    Median Imputation   
 40   LightGBM       NaN           0.001    Median Imputation   
 41   LightGBM       NaN             0.0    Median Imputation   
 42   LightGBM       NaN             100    Median Imputation   
 43   LightGBM       NaN            None    Median Imputation   
 44   LightGBM       NaN              31    Median Imputation   
 45   LightGBM       NaN            None    Median Imputation   
 46   LightGBM       NaN            None    Median Imputation   
 47   LightGBM       NaN             0.0    Median Imputation   
 48   LightGBM       NaN             0.0    Median Imputation   
 49   LightGBM       NaN             1.0    Median Imputation   
 50   LightGBM       NaN          200000    Median Imputation   
 51   LightGBM       NaN               0    Median Imputation   
 52   LightGBM  0.891493             NaN    Median Imputation   
 53   LightGBM  0.133550             NaN    Median Imputation   
 54   LightGBM  0.219251             NaN    Median Imputation   
 55   LightGBM  0.165992             NaN    Median Imputation   
 56   LightGBM  0.690328             NaN    Median Imputation   
 57   LightGBM  0.289697             NaN    Median Imputation   
 58   LightGBM  0.380657             NaN    Median Imputation   
 59   LightGBM       NaN            gbdt    Median Imputation   
 60   LightGBM       NaN            None    Median Imputation   
 61   LightGBM       NaN             1.0    Median Imputation   
 62   LightGBM       NaN           split    Median Imputation   
 63   LightGBM       NaN             0.1    Median Imputation   
 64   LightGBM       NaN              -1    Median Imputation   
 65   LightGBM       NaN              20    Median Imputation   
 66   LightGBM       NaN           0.001    Median Imputation   
 67   LightGBM       NaN             0.0    Median Imputation   
 68   LightGBM       NaN             100    Median Imputation   
 69   LightGBM       NaN            None    Median Imputation   
 70   LightGBM       NaN              31    Median Imputation   
 71   LightGBM       NaN            None    Median Imputation   
 72   LightGBM       NaN            None    Median Imputation   
 73   LightGBM       NaN             0.0    Median Imputation   
 74   LightGBM       NaN             0.0    Median Imputation   
 75   LightGBM       NaN             1.0    Median Imputation   
 76   LightGBM       NaN          200000    Median Imputation   
 77   LightGBM       NaN               0    Median Imputation   
 78   LightGBM  0.891465             NaN    Median Imputation   
 79   LightGBM  0.119718             NaN    Median Imputation   
 80   LightGBM  0.173469             NaN    Median Imputation   
 81   LightGBM  0.141667             NaN    Median Imputation   
 82   LightGBM  0.682028             NaN    Median Imputation   
 83   LightGBM  0.282222             NaN    Median Imputation   
 84   LightGBM  0.364056             NaN    Median Imputation   
 85   LightGBM       NaN            gbdt    Median Imputation   
 86   LightGBM       NaN            None    Median Imputation   
 87   LightGBM       NaN             1.0    Median Imputation   
 88   LightGBM       NaN           split    Median Imputation   
 89   LightGBM       NaN             0.1    Median Imputation   
 90   LightGBM       NaN              -1    Median Imputation   
 91   LightGBM       NaN              20    Median Imputation   
 92   LightGBM       NaN           0.001    Median Imputation   
 93   LightGBM       NaN             0.0    Median Imputation   
 94   LightGBM       NaN             100    Median Imputation   
 95   LightGBM       NaN            None    Median Imputation   
 96   LightGBM       NaN              31    Median Imputation   
 97   LightGBM       NaN            None    Median Imputation   
 98   LightGBM       NaN            None    Median Imputation   
 99   LightGBM       NaN             0.0    Median Imputation   
 100  LightGBM       NaN             0.0    Median Imputation   
 101  LightGBM       NaN             1.0    Median Imputation   
 102  LightGBM       NaN          200000    Median Imputation   
 103  LightGBM       NaN               0    Median Imputation   
 104  LightGBM  0.893309             NaN    Median Imputation   
 105  LightGBM  0.151292             NaN    Median Imputation   
 106  LightGBM  0.189815             NaN    Median Imputation   
 107  LightGBM  0.168378             NaN    Median Imputation   
 108  LightGBM  0.692653             NaN    Median Imputation   
 109  LightGBM  0.329428             NaN    Median Imputation   
 110  LightGBM  0.385305             NaN    Median Imputation   
 111  LightGBM       NaN            gbdt    Median Imputation   
 112  LightGBM       NaN            None    Median Imputation   
 113  LightGBM       NaN             1.0    Median Imputation   
 114  LightGBM       NaN           split    Median Imputation   
 115  LightGBM       NaN             0.1    Median Imputation   
 116  LightGBM       NaN              -1    Median Imputation   
 117  LightGBM       NaN              20    Median Imputation   
 118  LightGBM       NaN           0.001    Median Imputation   
 119  LightGBM       NaN             0.0    Median Imputation   
 120  LightGBM       NaN             100    Median Imputation   
 121  LightGBM       NaN            None    Median Imputation   
 122  LightGBM       NaN              31    Median Imputation   
 123  LightGBM       NaN            None    Median Imputation   
 124  LightGBM       NaN            None    Median Imputation   
 125  LightGBM       NaN             0.0    Median Imputation   
 126  LightGBM       NaN             0.0    Median Imputation   
 127  LightGBM       NaN             1.0    Median Imputation   
 128  LightGBM       NaN          200000    Median Imputation   
 129  LightGBM       NaN               0    Median Imputation   
 
         Imbalance Class Technique  \
 0    Hybrid Sampling (SMOTETomek)   
 1    Hybrid Sampling (SMOTETomek)   
 2    Hybrid Sampling (SMOTETomek)   
 3    Hybrid Sampling (SMOTETomek)   
 4    Hybrid Sampling (SMOTETomek)   
 5    Hybrid Sampling (SMOTETomek)   
 6    Hybrid Sampling (SMOTETomek)   
 7    Hybrid Sampling (SMOTETomek)   
 8    Hybrid Sampling (SMOTETomek)   
 9    Hybrid Sampling (SMOTETomek)   
 10   Hybrid Sampling (SMOTETomek)   
 11   Hybrid Sampling (SMOTETomek)   
 12   Hybrid Sampling (SMOTETomek)   
 13   Hybrid Sampling (SMOTETomek)   
 14   Hybrid Sampling (SMOTETomek)   
 15   Hybrid Sampling (SMOTETomek)   
 16   Hybrid Sampling (SMOTETomek)   
 17   Hybrid Sampling (SMOTETomek)   
 18   Hybrid Sampling (SMOTETomek)   
 19   Hybrid Sampling (SMOTETomek)   
 20   Hybrid Sampling (SMOTETomek)   
 21   Hybrid Sampling (SMOTETomek)   
 22   Hybrid Sampling (SMOTETomek)   
 23   Hybrid Sampling (SMOTETomek)   
 24   Hybrid Sampling (SMOTETomek)   
 25   Hybrid Sampling (SMOTETomek)   
 26   Hybrid Sampling (SMOTETomek)   
 27   Hybrid Sampling (SMOTETomek)   
 28   Hybrid Sampling (SMOTETomek)   
 29   Hybrid Sampling (SMOTETomek)   
 30   Hybrid Sampling (SMOTETomek)   
 31   Hybrid Sampling (SMOTETomek)   
 32   Hybrid Sampling (SMOTETomek)   
 33   Hybrid Sampling (SMOTETomek)   
 34   Hybrid Sampling (SMOTETomek)   
 35   Hybrid Sampling (SMOTETomek)   
 36   Hybrid Sampling (SMOTETomek)   
 37   Hybrid Sampling (SMOTETomek)   
 38   Hybrid Sampling (SMOTETomek)   
 39   Hybrid Sampling (SMOTETomek)   
 40   Hybrid Sampling (SMOTETomek)   
 41   Hybrid Sampling (SMOTETomek)   
 42   Hybrid Sampling (SMOTETomek)   
 43   Hybrid Sampling (SMOTETomek)   
 44   Hybrid Sampling (SMOTETomek)   
 45   Hybrid Sampling (SMOTETomek)   
 46   Hybrid Sampling (SMOTETomek)   
 47   Hybrid Sampling (SMOTETomek)   
 48   Hybrid Sampling (SMOTETomek)   
 49   Hybrid Sampling (SMOTETomek)   
 50   Hybrid Sampling (SMOTETomek)   
 51   Hybrid Sampling (SMOTETomek)   
 52   Hybrid Sampling (SMOTETomek)   
 53   Hybrid Sampling (SMOTETomek)   
 54   Hybrid Sampling (SMOTETomek)   
 55   Hybrid Sampling (SMOTETomek)   
 56   Hybrid Sampling (SMOTETomek)   
 57   Hybrid Sampling (SMOTETomek)   
 58   Hybrid Sampling (SMOTETomek)   
 59   Hybrid Sampling (SMOTETomek)   
 60   Hybrid Sampling (SMOTETomek)   
 61   Hybrid Sampling (SMOTETomek)   
 62   Hybrid Sampling (SMOTETomek)   
 63   Hybrid Sampling (SMOTETomek)   
 64   Hybrid Sampling (SMOTETomek)   
 65   Hybrid Sampling (SMOTETomek)   
 66   Hybrid Sampling (SMOTETomek)   
 67   Hybrid Sampling (SMOTETomek)   
 68   Hybrid Sampling (SMOTETomek)   
 69   Hybrid Sampling (SMOTETomek)   
 70   Hybrid Sampling (SMOTETomek)   
 71   Hybrid Sampling (SMOTETomek)   
 72   Hybrid Sampling (SMOTETomek)   
 73   Hybrid Sampling (SMOTETomek)   
 74   Hybrid Sampling (SMOTETomek)   
 75   Hybrid Sampling (SMOTETomek)   
 76   Hybrid Sampling (SMOTETomek)   
 77   Hybrid Sampling (SMOTETomek)   
 78   Hybrid Sampling (SMOTETomek)   
 79   Hybrid Sampling (SMOTETomek)   
 80   Hybrid Sampling (SMOTETomek)   
 81   Hybrid Sampling (SMOTETomek)   
 82   Hybrid Sampling (SMOTETomek)   
 83   Hybrid Sampling (SMOTETomek)   
 84   Hybrid Sampling (SMOTETomek)   
 85   Hybrid Sampling (SMOTETomek)   
 86   Hybrid Sampling (SMOTETomek)   
 87   Hybrid Sampling (SMOTETomek)   
 88   Hybrid Sampling (SMOTETomek)   
 89   Hybrid Sampling (SMOTETomek)   
 90   Hybrid Sampling (SMOTETomek)   
 91   Hybrid Sampling (SMOTETomek)   
 92   Hybrid Sampling (SMOTETomek)   
 93   Hybrid Sampling (SMOTETomek)   
 94   Hybrid Sampling (SMOTETomek)   
 95   Hybrid Sampling (SMOTETomek)   
 96   Hybrid Sampling (SMOTETomek)   
 97   Hybrid Sampling (SMOTETomek)   
 98   Hybrid Sampling (SMOTETomek)   
 99   Hybrid Sampling (SMOTETomek)   
 100  Hybrid Sampling (SMOTETomek)   
 101  Hybrid Sampling (SMOTETomek)   
 102  Hybrid Sampling (SMOTETomek)   
 103  Hybrid Sampling (SMOTETomek)   
 104  Hybrid Sampling (SMOTETomek)   
 105  Hybrid Sampling (SMOTETomek)   
 106  Hybrid Sampling (SMOTETomek)   
 107  Hybrid Sampling (SMOTETomek)   
 108  Hybrid Sampling (SMOTETomek)   
 109  Hybrid Sampling (SMOTETomek)   
 110  Hybrid Sampling (SMOTETomek)   
 111  Hybrid Sampling (SMOTETomek)   
 112  Hybrid Sampling (SMOTETomek)   
 113  Hybrid Sampling (SMOTETomek)   
 114  Hybrid Sampling (SMOTETomek)   
 115  Hybrid Sampling (SMOTETomek)   
 116  Hybrid Sampling (SMOTETomek)   
 117  Hybrid Sampling (SMOTETomek)   
 118  Hybrid Sampling (SMOTETomek)   
 119  Hybrid Sampling (SMOTETomek)   
 120  Hybrid Sampling (SMOTETomek)   
 121  Hybrid Sampling (SMOTETomek)   
 122  Hybrid Sampling (SMOTETomek)   
 123  Hybrid Sampling (SMOTETomek)   
 124  Hybrid Sampling (SMOTETomek)   
 125  Hybrid Sampling (SMOTETomek)   
 126  Hybrid Sampling (SMOTETomek)   
 127  Hybrid Sampling (SMOTETomek)   
 128  Hybrid Sampling (SMOTETomek)   
 129  Hybrid Sampling (SMOTETomek)   
 
                                      Model Unique Code  
 0    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 1    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 2    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 3    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 4    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 5    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 6    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 7    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 8    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 9    LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 10   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 11   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 12   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 13   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 14   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 15   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 16   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 17   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 18   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 19   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 20   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 21   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 22   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 23   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 24   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 25   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 26   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 27   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 28   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 29   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 30   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 31   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 32   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 33   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 34   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 35   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 36   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 37   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 38   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 39   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 40   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 41   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 42   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 43   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 44   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 45   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 46   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 47   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 48   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 49   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 50   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 51   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 52   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 53   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 54   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 55   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 56   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 57   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 58   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 59   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 60   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 61   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 62   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 63   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 64   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 65   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 66   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 67   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 68   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 69   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 70   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 71   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 72   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 73   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 74   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 75   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 76   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 77   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 78   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 79   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 80   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 81   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 82   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 83   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 84   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 85   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 86   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 87   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 88   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 89   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 90   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 91   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 92   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 93   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 94   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 95   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 96   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 97   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 98   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 99   LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 100  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 101  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 102  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 103  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 104  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 105  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 106  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 107  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 108  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 109  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 110  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 111  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 112  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 113  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 114  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 115  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 116  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 117  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 118  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 119  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 120  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 121  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 122  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 123  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 124  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 125  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 126  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 127  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 128  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  
 129  LightGBM_Hybrid Sampling (SMOTETomek)_Median I...  ]

import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score

def calculate_classification_metrics_one_class(y_true, y_pred_prob, target_class=1):
    # Calculate binary predictions
    y_pred = (y_pred_prob >= 0.5).astype(int)

    # Confusion Matrix
    cm = confusion_matrix(y_true, y_pred)

    # True Positives, True Negatives, False Positives, False Negatives
    TP, TN, FP, FN = cm.ravel()

    # Sensitivity (True Positive Rate)
    sensitivity = TP / (TP + FN)

    # Specificity (True Negative Rate)
    specificity = TN / (TN + FP)

    # Area Under the ROC Curve (AUC-ROC)
    fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
    auc_roc = auc(fpr, tpr)

    # Area Under the Precision-Recall Curve (AUC-PRC)
    precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
    auc_prc = auc(recall, precision)

    # Gini Coefficient
    gini = 2 * auc_roc - 1

    # Kolmogorov-Smirnov (KS) statistic
    ks = np.max(tpr - fpr)

    # Matthews Correlation Coefficient (MCC)
    mcc = matthews_corrcoef(y_true, y_pred)

    # Cohen's Kappa
    kappa = cohen_kappa_score(y_true, y_pred)

    # Accuracy
    accuracy = accuracy_score(y_true, y_pred)

    # Balanced Accuracy
    balanced_accuracy = balanced_accuracy_score(y_true, y_pred)

    # F1 Score
    f1 = f1_score(y_true, y_pred)

    # Weighted F1 Score
    f1_weighted = f1_score(y_true, y_pred, average='weighted')

    # Precision
    precision = precision_score(y_true, y_pred)

    # Recall
    recall = recall_score(y_true, y_pred)

     # Create DataFrame
    metrics_df = pd.DataFrame({
        'Class': [f'Class {target_class}'],
        'TP': [TP],
        'TN': [TN],
        'FP': [FP],
        'FN': [FN],
        'Sensitivity_TPR': [sensitivity],
        'Specificity': [specificity],
        'AUC_ROC': [auc_roc],
        'AUC_PRC': [auc_prc],
        'Gini': [gini],
        'KS': [ks],
        'MCC': [mcc],
        'Kappa': [kappa],
        'Accuracy': [accuracy],
        'Balanced_Accuracy': [balanced_accuracy],
        'F1': [f1],
        'F1_Weighted': [f1_weighted],
        'Precision': [precision],
        'Recall': [recall]
    })

    # Reset index
    metrics_df = metrics_df.reset_index(drop=True)

    return metrics_df

imputation_technique

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier

import os
import json

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the imbalanced data handling techniques
sampling_techniques = {
    'None': None,
    'Oversampling': RandomOverSampler(),
    'Undersampling': RandomUnderSampler(),
}

# Define the imputation techniques
imputation_techniques = {
    'None': None,
    'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
    'Mean Imputation': SimpleImputer(strategy='mean'),
    'Median Imputation': SimpleImputer(strategy='median')
}

# Create a list to store the results
results_2 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [
    # ('Logistic Regression', LogisticRegression()),
     ('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
    # ('Support Vector Machines (SVM)', SVC(probability=True)),
    # ('Neural Networks', MLPClassifier()),
    # ('Naive Bayes', GaussianNB()),
    # ('Decision Trees', DecisionTreeClassifier()),
    # ('Random Forest', RandomForestClassifier()),
    # ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
    # ('XGBoost', XGBClassifier()),
    # ('LightGBM', LGBMClassifier()),
    # ('CatBoost', CatBoostClassifier()),
    # ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())



]:
    # Modify hyperparameters for the current model to include both 'None' and 'balanced'
    models_to_try_both_hyperparams = ['Logistic Regression', 'Support Vector Machines (SVM)', 'Naive Bayes', 'Decision Trees', 'Random Forest', 'HistGradientBoostingClassifier']
    hyperparameters[model_name] = {'class_weight': [None, 'balanced']} if model_name in models_to_try_both_hyperparams else {}

    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():
        # Iterate through each hyperparameter
        for hyperparameter in hyperparameters[model_name].get('class_weight', [None]):
            # Iterate through each imputation technique
            for imputation_name, imputation_technique in imputation_techniques.items():
                # Create a new dataframe to store the results for this model, technique combination
                df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])


                # Perform k-fold cross-validation
                for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                    X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                    y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                    # Apply imputation technique on the train and test sets, if imputation_name is not None
                    if imputation_name is not None and imputation_technique is not None:
                        print(imputation_name)
                        X_train_imputed = imputation_technique.fit_transform(X_train)
                        X_test_imputed = imputation_technique.transform(X_test)
                    else:
                        print(imputation_name)
                        if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                            X_train_imputed, X_test_imputed = X_train, X_test
                        else:
                            break

                    # Apply the imbalanced data handling technique on the train set
                    if sampling_technique is not None:
                        X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
                    else:
                        X_train_resampled, y_train_resampled = X_train_imputed, y_train


                    # Train the model on the resampled train set
                    model.fit(X_train_resampled, y_train_resampled)

                    # Make predictions on the test set
                    y_pred = model.predict(X_test_imputed)
                    y_prob = model.predict_proba(X_test_imputed)[:, 1]

                    # Calculate the evaluation metrics
                    metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                    # Set 'Imputation Technique' based on the specified conditions
                    if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                        imputation_name = 'None'
                    else:
                        imputation_name = imputation_name

                    #imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name


                    # Create a new DataFrame for each iteration
                    df_result_2 = pd.DataFrame({
                        'Algorithm': model_name,
                        'Hyperparameters': [model.get_params()],
                        'Imputation Technique': imputation_name,
                        'Imbalance Class Technique': sampling_name,
                        'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
                        **metrics_df  # Unpack the metrics_df columns
                    })

                    # Append the result to the list
                    results_2.append(df_result_2)

# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
    df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_2.csv", index=False)

# Display the final DataFrame
display(df_results_final_2)

None
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation
None
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation
None
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Zero Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Mean Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation
Median Imputation

model

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB, ComplementNB, MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier
import os
import json

# Redefine function without print
def end_timer():
    end_time = time.time()
    execution_time = end_time - start_time
    execution_time = round(execution_time, 2)
    return execution_time

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the imbalanced data handling techniques
sampling_techniques = {
    'None': None,
    'Oversampling': RandomOverSampler(),
    'Undersampling': RandomUnderSampler(),
}

# Define the imputation techniques
imputation_techniques = {
    'None': None,
    'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
    'Mean Imputation': SimpleImputer(strategy='mean'),
    'Median Imputation': SimpleImputer(strategy='median')
}

# Create a list to store the results
results_2 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [
     ('Logistic Regression', LogisticRegression(hyperparameter)),
     ('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
    # ('Support Vector Machines (SVM)', SVC(hyperparameters, probability=True)),
     ('Neural Networks', MLPClassifier()),
     ('Naive Bayes',GaussianNB()),
     ('Decision Trees', DecisionTreeClassifier()),
     ('Random Forest', RandomForestClassifier()),
     ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
     ('XGBoost', XGBClassifier()),
     ('LightGBM', LGBMClassifier()),
    # ('CatBoost', CatBoostClassifier()),
     ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())



]:
    # Modify hyperparameters for the current model to include both 'None' and 'balanced'
    models_to_try_both_hyperparams = ['Logistic Regression', 'Support Vector Machines (SVM)', 'Decision Trees', 'Random Forest', 'HistGradientBoostingClassifier']
    hyperparameters = {'class_weight': [None, 'balanced']} if model_name in models_to_try_both_hyperparams else {}
    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():

        # Iterate through each hyperparameter
        for hyperparameter in hyperparameters.get('class_weight', [None]):
            if model_name in models_to_try_both_hyperparams:
             # Set hyperparameters for the model
                model.set_params(**{'class_weight': hyperparameter})
            else:
                pass
            # Iterate through each imputation technique
            for imputation_name, imputation_technique in imputation_techniques.items():

                # Create a new dataframe to store the results for this model, technique combination
                df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code', 'Execution Time', 'Shape_X0_0', 'Shape_X0_1', 'Shape_X1_0', 'Shape_X1_1'])


                # Perform k-fold cross-validation
                for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                    Init_Num_Class_0, Init_Num_Class_1, Res_Num_Class_0, Res_Num_Class_1, X_train_resampled, y_train_resampled, X_train, y_train = 0, 0, 0, 0, 0, 0, 0, 0
                    X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                    y_train, y_test = target.iloc[train_index], target.iloc[test_index]


                    # Start timer
                    start_timer()

                    # Apply imputation technique on the train and test sets, if imputation_name is not None
                    if imputation_name is not None and imputation_technique is not None:
                        X_train_imputed = imputation_technique.fit_transform(X_train)
                        X_test_imputed = imputation_technique.transform(X_test)
                    else:
                        if model_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                            X_train_imputed, X_test_imputed = X_train, X_test
                        else:
                            break

                    # Apply the imbalanced data handling technique on the train set
                    if sampling_technique is not None:
                        X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
                    else:
                        X_train_resampled, y_train_resampled = X_train_imputed, y_train

                    # Stop timer

                    execution_time = end_timer()

                    # Train the model on the resampled train set
                    model.fit(X_train_resampled, y_train_resampled)

                    # Make predictions on the test set
                    y_pred = model.predict(X_test_imputed)
                    y_prob = model.predict_proba(X_test_imputed)[:, 1]

                    # Calculate the evaluation metrics
                    metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                    # Set 'Imputation Technique' based on the specified conditions
                    if imputation_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                        imputation_name = 'None'
                    else:
                        imputation_name = imputation_name

                    # Count samples and features for each label before resampling
                    Init_Num_Class_0 = X_train[y_train == 0].shape[0]  # Number of samples with label 0
                    Init_Num_Class_1 = X_train[y_train == 1].shape[0]  # Number of samples with label 1
                    Res_Num_Class_0 = X_train_resampled[y_train_resampled == 0].shape[0]  # Number of features with label 0 after resampling
                    Res_Num_Class_1 = X_train_resampled[y_train_resampled == 1].shape[0]  # Number of features with label 1 after resampling

                    # Append the result to the list
                    df_result_2 = pd.DataFrame({
                        'Algorithm': model_name,
                        'Hyperparameters': [model.get_params()],
                        'Imputation Technique': imputation_name,
                        'Imbalance Class Technique': sampling_name,
                        'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_{hyperparameter}_fold_{fold}",
                        'Execution Time': execution_time,
                        'Init_Num_Class_0': Init_Num_Class_0,
                        'Init_Num_Class_1': Init_Num_Class_1,
                        'Res_Num_Class_0': Res_Num_Class_0,
                        'Res_Num_Class_1': Res_Num_Class_1,
                        'execution_time_seg': execution_time,
                        **metrics_df  # Unpack the metrics_df columns
                    })

                    # Append the result to the list
                    results_2.append(df_result_2)

# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
    df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_2.csv", index=False)

# Display the final DataFrame
display(df_results_final_2)

[LightGBM] [Info] Number of positive: 763, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60337
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321
[LightGBM] [Info] Start training from score -2.939321
[LightGBM] [Info] Number of positive: 836, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60388
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877
[LightGBM] [Info] Start training from score -2.842877
[LightGBM] [Info] Number of positive: 813, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60401
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376
[LightGBM] [Info] Start training from score -2.872376
[LightGBM] [Info] Number of positive: 804, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60578
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203
[LightGBM] [Info] Start training from score -2.884203
[LightGBM] [Info] Number of positive: 784, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60304
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783
[LightGBM] [Info] Start training from score -2.910783
[LightGBM] [Info] Number of positive: 763, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59971
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321
[LightGBM] [Info] Start training from score -2.939321
[LightGBM] [Info] Number of positive: 836, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60014
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877
[LightGBM] [Info] Start training from score -2.842877
[LightGBM] [Info] Number of positive: 813, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021057 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60026
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376
[LightGBM] [Info] Start training from score -2.872376
[LightGBM] [Info] Number of positive: 804, number of negative: 14383
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 60211
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203
[LightGBM] [Info] Start training from score -2.884203
[LightGBM] [Info] Number of positive: 784, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59920
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783
[LightGBM] [Info] Start training from score -2.910783
[LightGBM] [Info] Number of positive: 763, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62019
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321
[LightGBM] [Info] Start training from score -2.939321
[LightGBM] [Info] Number of positive: 836, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62314
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877
[LightGBM] [Info] Start training from score -2.842877
[LightGBM] [Info] Number of positive: 813, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61847
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376
[LightGBM] [Info] Start training from score -2.872376
[LightGBM] [Info] Number of positive: 804, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62515
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203
[LightGBM] [Info] Start training from score -2.884203
[LightGBM] [Info] Number of positive: 784, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62093
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783
[LightGBM] [Info] Start training from score -2.910783
[LightGBM] [Info] Number of positive: 763, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59911
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321
[LightGBM] [Info] Start training from score -2.939321
[LightGBM] [Info] Number of positive: 836, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59950
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877
[LightGBM] [Info] Start training from score -2.842877
[LightGBM] [Info] Number of positive: 813, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59972
[LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376
[LightGBM] [Info] Start training from score -2.872376
[LightGBM] [Info] Number of positive: 804, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60146
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203
[LightGBM] [Info] Start training from score -2.884203
[LightGBM] [Info] Number of positive: 784, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 59870
[LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783
[LightGBM] [Info] Start training from score -2.910783
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048959 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61462
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61473
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61372
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053134 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61680
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61379
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030900 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61103
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61139
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61121
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025948 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61321
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61054
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053725 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60769
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60848
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054027 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60552
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041971 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61150
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60682
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60880
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60975
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60915
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029968 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61130
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60858
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26520
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27953
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27552
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27531
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26861
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 25547
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26754
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004938 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27339
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26352
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 358
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26046
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007831 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 31496
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32977
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32893
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33481
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32304
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28686
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 358
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30197
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 362
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30877
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29923
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29815
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 366
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000

# Order df_results_final_2 by 'Execution Time'
df_results_final_2 = df_results_final_2.sort_values(by=['Execution Time'], ascending=False)
display(df_results_final_2)

# Sum 'Execution Time' by 'Algorithm' and divide by count of entries of that Algorithm
df_exec_time  = (df_results_final_2.groupby('Algorithm')['Execution Time'].sum() / df_results_final_2.groupby('Algorithm')['Execution Time'].count())*df_results_final_2.groupby('Algorithm')['Execution Time'].count().sum()
df_exec_time = df_exec_time.sort_values(ascending=False)
df_exec_time

Algorithm
Logistic Regression                 342.00
k-Nearest Neighbors (k-NN)          341.76
Naive Bayes                         340.64
Neural Networks                     337.28
Gradient Boosting Machines (GBM)    332.16
Random Forest                       331.76
LightGBM                            301.32
XGBoost                             272.76
Decision Trees                      269.16
HistGradientBoostingClassifier      268.44
Name: Execution Time, dtype: float64

df_results_final_2.groupby('Algorithm')['Execution Time'].count().sum()

720

import seaborn as sns
import matplotlib.pyplot as plt

# Extract relevant data for plotting
data_to_plot = df_results_final_2[['Algorithm', 'Imbalance Class Technique', 'Imputation Technique', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Model Unique Code', 'Execution Time']]

# Extract fold number from 'Model Unique Code'
data_to_plot['Fold'] = data_to_plot['Model Unique Code'].str.extract(r'fold_(\d+)').astype(int)
# Exclude the text _fold_ and the fold number from 'Model Unique Code'
data_to_plot['Model Code'] = data_to_plot['Model Unique Code'].str.extract(r'^(.*?)_fold_\d+').astype(str)

# Melt the dataframe to long format for easier plotting
melted_data = pd.melt(data_to_plot, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
                      value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
                      var_name='Metrics', value_name='Metric Value')

# Create separate boxplots for each metric
metrics = melted_data['Metrics'].unique()

for metric in metrics:
    plt.figure(figsize=(21, 7))
    sns.set(style='whitegrid')
    sns.boxplot(x='Model Code', y='Metric Value', hue='Imbalance Class Technique',
                data=melted_data[melted_data['Metrics'] == metric], palette='Set3', showfliers=False, dodge=True)
    plt.title(f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight')
    plt.xlabel('Model Code')
    plt.ylabel('Metric Value')
    plt.xticks(rotation=90, fontsize=8)
    plt.legend(title='Imbalance Class Technique')
    plt.show()

import plotly.express as px

# Create a function to generate boxplots for each metric
def plot_metric_boxplots(data, metric):
    fig = px.box(data, x='Model Code', y='Metric Value', color='Imbalance Class Technique',
                 category_orders={'Imbalance Class Technique': ['None', 'Over-sampling', 'Under-sampling']},
                 points=False,
                 title=f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight',
                 labels={'Metric Value': metric, 'Model Code': 'Model Code'},
                 width=1200, height=600)

    # Customize the layout

    fig.update_layout(xaxis=dict(tickangle=90, showticklabels=False), legend_title_text='Imbalance Class Technique')
    #fig.update_layout(xaxis=dict(tickangle=90), legend_title_text='Imbalance Class Technique')

    # Show the plot
    fig.show()

# Melt the dataframe to long format for easier plotting
melted_data = pd.melt(data_to_plot, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
                      value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
                      var_name='Metrics', value_name='Metric Value')


# Create boxplots for each metric
for metric in melted_data['Metrics'].unique():
    # Filter relevant columns for hover_data
    relevant_columns = ['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold', 'Metrics', 'Metric Value']
    plot_metric_boxplots(melted_data[melted_data['Metrics'] == metric][relevant_columns], metric)

# List 'Algorithm' with AUC_ROC > 0.65
list_AUC_ROC = df_results_final_2[df_results_final_2['AUC_ROC'] > 0.65]['Algorithm'].unique()
# List 'Algorithm' with KS > 0.3
list_KS = df_results_final_2[df_results_final_2['KS'] > 0.35]['Algorithm'].unique()
# Lis 'Algorithm' with Balanced_Accuracy > 0.60
List_Bal_Acc = df_results_final_2[df_results_final_2['Balanced_Accuracy'] > 0.65]['Algorithm'].unique()
# Get the intersection of the above three lists
list_intersection = (list(set(list_AUC_ROC).intersection(list_KS).intersection(List_Bal_Acc)))
print(list_AUC_ROC, list_KS, List_Bal_Acc, sep='\n')
print(list_intersection)

['LightGBM' 'XGBoost' 'HistGradientBoostingClassifier'
 'Gradient Boosting Machines (GBM)' 'Logistic Regression' 'Random Forest'
 'Neural Networks' 'k-Nearest Neighbors (k-NN)']
['LightGBM' 'Gradient Boosting Machines (GBM)'
 'HistGradientBoostingClassifier' 'Random Forest' 'XGBoost']
['Gradient Boosting Machines (GBM)' 'HistGradientBoostingClassifier'
 'XGBoost' 'Random Forest' 'LightGBM']
['LightGBM', 'XGBoost', 'HistGradientBoostingClassifier', 'Gradient Boosting Machines (GBM)', 'Random Forest']

data_to_plot

df_results_final_2.columns

Index(['Algorithm', 'Hyperparameters', 'Imputation Technique',
       'Imbalance Class Technique', 'Model Unique Code', 'Execution Time',
       'Init_Num_Class_0', 'Init_Num_Class_1', 'Res_Num_Class_0',
       'Res_Num_Class_1', 'execution_time', 'Class', 'TP', 'TN', 'FP', 'FN',
       'Sensitivity_TPR', 'Specificity', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS',
       'MCC', 'Kappa', 'Accuracy', 'Balanced_Accuracy', 'F1', 'F1_Weighted',
       'Precision', 'Recall'],
      dtype='object')

metrics_df

# Set the display character limit to 100 and max number of columns to 100
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_colwidth', 100)

display(df_results_final_2)

10. Treinamento de Modelo¶

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.experimental import enable_hist_gradient_boosting
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
import os
import json
import time

# Redefine function without print
def end_timer():
    end_time = time.time()
    execution_time = end_time - start_time
    execution_time = round(execution_time, 2)
    return execution_time

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the imbalanced data handling techniques
sampling_techniques = {
    'None': None,
    'Oversampling': RandomOverSampler(),
    'Undersampling': RandomUnderSampler(),
}

# Define the imputation techniques
imputation_techniques = {
    'None': None,
    'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
}

# Create a list to store the results
results_3 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=10, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [
    ('Random Forest', RandomForestClassifier()),
    ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
    ('XGBoost', XGBClassifier()),
    ('LightGBM', LGBMClassifier()),
    ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())
]:
    # Filter only specified algorithms
    if model_name not in ['LightGBM', 'XGBoost', 'HistGradientBoostingClassifier', 'Gradient Boosting Machines (GBM)', 'Random Forest']:
        continue

    # Define the normalization and scaling techniques
    normalization_scalers = {
        'MinMaxScaler [0, 1]': MinMaxScaler(feature_range=(0, 1)),
        'MinMaxScaler [1, 2]': MinMaxScaler(feature_range=(1, 2)),
        'StandardScaler': StandardScaler(),
        'StandardScaler MinMaxScaler [0, 1]': [StandardScaler(), MinMaxScaler(feature_range=(0, 1))]
    }

    # Iterate through each normalization and scaling technique
    for scaler_name, scaler in normalization_scalers.items():

        # Iterate through each imbalanced data handling technique
        for sampling_name, sampling_technique in sampling_techniques.items():

            # Iterate through each imputation technique
            for imputation_name, imputation_technique in imputation_techniques.items():

                # Create a new dataframe to store the results for this model, technique combination
                df_result_3 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code', 'Execution Time', 'Shape_X0_0', 'Shape_X0_1', 'Shape_X1_0', 'Shape_X1_1'])

                # Perform k-fold cross-validation
                for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                    Init_Num_Class_0, Init_Num_Class_1, Res_Num_Class_0, Res_Num_Class_1, X_train_resampled, y_train_resampled, X_train, y_train = 0, 0, 0, 0, 0, 0, 0, 0
                    X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                    y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                    # Apply imputation technique on the train and test sets, if imputation_name is not None
                    if imputation_name is not None and imputation_technique is not None:
                        X_train_imputed = imputation_technique.fit_transform(X_train)
                        X_test_imputed = imputation_technique.transform(X_test)
                    else:
                        if model_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                            X_train_imputed, X_test_imputed = X_train, X_test
                        else:
                            break

                    # Apply normalization and scaling technique on the train and test sets
                    if isinstance(scaler, list):
                        # For multiple scalers, apply them sequentially
                        for s in scaler:
                            X_train_imputed = s.fit_transform(X_train_imputed)
                            X_test_imputed = s.transform(X_test_imputed)
                    else:
                        X_train_imputed = scaler.fit_transform(X_train_imputed)
                        X_test_imputed = scaler.transform(X_test_imputed)

                    # Apply the imbalanced data handling technique on the train set
                    if sampling_technique is not None:
                        X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
                    else:
                        X_train_resampled, y_train_resampled = X_train_imputed, y_train

                    # Start timer
                    start_time = time.time()

                    # Train the model on the resampled train set
                    model.fit(X_train_resampled, y_train_resampled)

                    # Stop timer
                    execution_time = end_timer()

                    # Make predictions on the test set
                    y_pred = model.predict(X_test_imputed)
                    y_prob = model.predict_proba(X_test_imputed)[:, 1]

                    # Calculate the evaluation metrics
                    metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                    # Set 'Imputation Technique' based on the specified conditions
                    if imputation_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                        imputation_name = 'None'
                    else:
                        imputation_name = imputation_name

                    # Count samples and features for each label before resampling
                    Init_Num_Class_0 = X_train[y_train == 0].shape[0]  # Number of samples with label 0
                    Init_Num_Class_1 = X_train[y_train == 1].shape[0]  # Number of samples with label 1
                    Res_Num_Class_0 = X_train_resampled[y_train_resampled == 0].shape[0]  # Number of features with label 0 after resampling
                    Res_Num_Class_1 = X_train_resampled[y_train_resampled == 1].shape[0]  # Number of features with label 1 after resampling

                    # Append the result to the list
                    df_result_3 = pd.DataFrame({
                        'Algorithm': model_name,
                        'Hyperparameters': [model.get_params()],
                        'Imputation Technique': imputation_name,
                        'Imbalance Class Technique': sampling_name,
                        'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_{scaler_name}_fold_{fold}",
                        'Execution Time': execution_time,
                        'Init_Num_Class_0': Init_Num_Class_0,
                        'Init_Num_Class_1': Init_Num_Class_1,
                        'Res_Num_Class_0': Res_Num_Class_0,
                        'Res_Num_Class_1': Res_Num_Class_1,
                        'execution_time_seg': execution_time,
                        **metrics_df  # Unpack the metrics_df columns
                    })

                    # Append the result to the list
                    results_3.append(df_result_3)

# Combine all the results DataFrames into one final DataFrame
df_results_final_3 = pd.concat(results_3, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_3['Model Unique Code'].unique():
    df_model = df_results_final_3[df_results_final_3['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_3.csv", index=False)

# Display the final DataFrame
display(df_results_final_3)

[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61413
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61402
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61361
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61498
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61484
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61459
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61537
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61551
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61470
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61416
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61009
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61052
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61002
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61149
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61131
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61103
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61133
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61201
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61117
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61061
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62418
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.067002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62525
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62378
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.066744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62547
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021989 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 62513
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070971 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62544
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62604
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62515
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073837 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62561
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62426
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62119
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035043 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62169
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038075 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62029
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62119
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033802 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62276
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031823 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62317
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62127
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62184
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037010 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62247
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62080
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28428
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009921 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28797
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29183
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29270
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29396
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29369
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28731
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29482
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008003 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28640
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28887
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004915 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28634
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29181
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005035 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29865
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29560
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29777
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004915 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29186
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29720
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29646
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 380
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29204
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29582
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63435
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63640
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63346
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63532
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63518
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.056939 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63541
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63672
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63705
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63644
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63324
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63032
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027051 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63155
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62892
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63131
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63113
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63138
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63265
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63158
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 63247
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62925
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62398
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62234
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62242
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62292
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62290
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62216
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62498
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62473
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62360
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62145
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61860
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61774
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039971 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61650
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61796
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041024 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61797
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61758
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62027
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039823 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 61919
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61884
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61669
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 31720
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32707
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33019
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32599
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33005
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32255
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007055 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32406
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32551
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 31534
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32678
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33485
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 35037
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34173
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33683
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34922
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 384
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34375
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 35025
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34905
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34978
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 35076
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.041815 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 61475
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61551
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61488
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61567
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61559
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61526
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61606
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61627
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61520
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61504
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61066
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61061
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61018
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024050 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61164
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61147
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61127
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61202
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61231
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61118
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61084
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62543
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62723
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62663
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62662
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62727
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058925 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62751
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.066769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62682
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62716
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62672
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62659
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62154
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62310
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62282
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044882 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62366
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62398
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62381
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62322
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62398
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62312
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62379
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29875
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30664
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006991 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30452
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 31238
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30293
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30749
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30668
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005933 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30651
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29877
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30791
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004961 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28601
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29023
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28998
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28599
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28488
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29079
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28551
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28972
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28134
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007210 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 28589
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61413
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61402
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61361
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61498
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61484
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61459
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61537
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61551
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036960 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61470
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61416
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 876, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61009
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894
[LightGBM] [Info] Start training from score -2.917894
[LightGBM] [Info] Number of positive: 887, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61052
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736
[LightGBM] [Info] Start training from score -2.904736
[LightGBM] [Info] Number of positive: 919, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61002
[LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318
[LightGBM] [Info] Start training from score -2.867318
[LightGBM] [Info] Number of positive: 917, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61149
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682
[LightGBM] [Info] Start training from score -2.869682
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61131
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 909, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61103
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939
[LightGBM] [Info] Start training from score -2.878939
[LightGBM] [Info] Number of positive: 904, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61133
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764
[LightGBM] [Info] Start training from score -2.884764
[LightGBM] [Info] Number of positive: 900, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61201
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445
[LightGBM] [Info] Start training from score -2.889445
[LightGBM] [Info] Number of positive: 879, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61117
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352
[LightGBM] [Info] Start training from score -2.914352
[LightGBM] [Info] Number of positive: 905, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61061
[LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596
[LightGBM] [Info] Start training from score -2.883596
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62441
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62531
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62338
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055987 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62480
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.064688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62537
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62585
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.056076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62528
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62564
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.065818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62539
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.057757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62439
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16208, number of negative: 16208
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62044
[LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16197, number of negative: 16197
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62063
[LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16165, number of negative: 16165
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62013
[LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16168, number of negative: 16168
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62079
[LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034024 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62216
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16176, number of negative: 16176
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62221
[LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16181, number of negative: 16181
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62122
[LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16185, number of negative: 16185
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62224
[LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16206, number of negative: 16206
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62169
[LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 16180, number of negative: 16180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033885 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 62147
[LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28784
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29177
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29142
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29321
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28874
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29148
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009792 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28661
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29546
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28761
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29201
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 876, number of negative: 876
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28844
[LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 380
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 887, number of negative: 887
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29364
[LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 919, number of negative: 919
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30121
[LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 917, number of negative: 917
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29597
[LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 374
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29662
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 909, number of negative: 909
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29611
[LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 382
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 904, number of negative: 904
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29764
[LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 900, number of negative: 900
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29616
[LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 879, number of negative: 879
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 28870
[LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 905, number of negative: 905
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29734
[LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000

# Order df_results_final_3 by 'Balanced_Accuracy'
display(df_results_final_3.sort_values(by=['Balanced_Accuracy'], ascending=False).head(10))
display(df_results_final_3.sort_values(by=['AUC_ROC'], ascending=False).head(10))
display(df_results_final_3.sort_values(by=['KS'], ascending=False).head(10))

11. Avaliação de Modelo¶

# Concat df_results_final_2 and df_results_final_3
df_results_final_concat = pd.concat([df_results_final_2, df_results_final_3])
df_results_final_concat

import seaborn as sns
import matplotlib.pyplot as plt

# Extract relevant data for plotting
data_to_plot_concat = df_results_final_concat[['Algorithm', 'Imbalance Class Technique', 'Imputation Technique', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Model Unique Code', 'Execution Time']]

# Extract fold number from 'Model Unique Code'
data_to_plot_concat['Fold'] = data_to_plot_concat['Model Unique Code'].str.extract(r'fold_(\d+)').astype(int)
# Exclude the text _fold_ and the fold number from 'Model Unique Code'
data_to_plot_concat['Model Code'] = data_to_plot_concat['Model Unique Code'].str.extract(r'^(.*?)_fold_\d+').astype(str)

# Melt the dataframe to long format for easier plotting
melted_data_concat = pd.melt(data_to_plot_concat, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
                      value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
                      var_name='Metrics', value_name='Metric Value')

# Create separate boxplots for each metric
metrics = melted_data_concat['Metrics'].unique()

for metric in metrics:
    plt.figure(figsize=(21, 7))
    sns.set(style='whitegrid')
    sns.boxplot(x='Model Code', y='Metric Value', hue='Imbalance Class Technique',
                data=melted_data_concat[melted_data_concat['Metrics'] == metric], palette='Set3', showfliers=False, dodge=True)
    plt.title(f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight')
    plt.xlabel('Model Code')
    plt.ylabel('Metric Value')
    plt.xticks(rotation=90, fontsize=8)
    plt.legend(title='Imbalance Class Technique')
    plt.show()

import plotly.express as px

# Create a function to generate boxplots for each metric
def plot_metric_boxplots(data, metric):
    fig = px.box(data, x='Model Code', y='Metric Value', color='Imbalance Class Technique',
                 category_orders={'Imbalance Class Technique': ['None', 'Over-sampling', 'Under-sampling']},
                 points=False,
                 title=f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, Class Weight and Normalization',
                 labels={'Metric Value': metric, 'Model Code': 'Model Code'},
                 width=1200, height=600)

    # Customize the layout

    fig.update_layout(xaxis=dict(tickangle=90, showticklabels=False), legend_title_text='Imbalance Class Technique')
    #fig.update_layout(xaxis=dict(tickangle=90), legend_title_text='Imbalance Class Technique')

    # Save the HTML file
    fig.write_html(f'MoDelEvaluation_{metric}_boxplot.html')
    # Show the plot
    fig.show()

# Melt the dataframe to long format for easier plotting
melted_data_concat = pd.melt(data_to_plot_concat, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
                      value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
                      var_name='Metrics', value_name='Metric Value')


# Create boxplots for each metric
for metric in melted_data_concat['Metrics'].unique():
    # Filter relevant columns for hover_data
    relevant_columns = ['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold', 'Metrics', 'Metric Value']
    plot_metric_boxplots(melted_data_concat[melted_data_concat['Metrics'] == metric][relevant_columns], metric)

data_to_plot_concat.columns

Index(['Algorithm', 'Imbalance Class Technique', 'Imputation Technique',
       'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted',
       'Model Unique Code', 'Execution Time', 'Fold', 'Model Code'],
      dtype='object')

# Sort the dataframe based on AUC_ROC, KS, and Balanced Accuracy
sorted_data_concat_AUC = data_to_plot_concat.sort_values(by='AUC_ROC', ascending=False)
sorted_data_concat_KS = data_to_plot_concat.sort_values(by='KS', ascending=False)
sorted_data_concat_BA = data_to_plot_concat.sort_values(by='Balanced_Accuracy', ascending=False)

# Select the top 30 models
top_30_models_AUC = sorted_data_concat_AUC.head(30)
top_30_models_KS = sorted_data_concat_KS.head(30)
top_30_models_BA = sorted_data_concat_BA.head(30)

# Print the list of each metric for the top 30 models
print("Top 30 Models based on AUC_ROC:")
display(top_30_models_AUC[['Model Code', 'AUC_ROC']])

print("\nTop 30 Models based on KS:")
display(top_30_models_KS[['Model Code', 'KS']])

print("\nTop 30 Models based on Balanced Accuracy:")
display(top_30_models_BA[['Model Code', 'Balanced_Accuracy']])

# Find the intersection of Model Codes from the three lists
intersection_model_codes = set(top_30_models_AUC['Model Code']).intersection(top_30_models_KS['Model Code'], top_30_models_BA['Model Code'])

# Print the intersection
print("\nIntersection of Model Codes:")
print(intersection_model_codes)

# Filter the DataFrame based on Model Codes in the intersection
best_10_models = data_to_plot_concat[data_to_plot_concat['Model Code'].isin(intersection_model_codes)]

# Display the filtered DataFrame soted by KS
best_10_models = best_10_models.sort_values(by='KS', ascending=False)
display(best_10_models)

Top 30 Models based on AUC_ROC:

Top 30 Models based on KS:

Top 30 Models based on Balanced Accuracy:

Intersection of Model Codes:
{'XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]', 'Random Forest_Undersampling_Zero Imputation_StandardScaler', 'Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]', 'Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]', 'HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]'}

data_to_plot_concat['Model Code'].nunique()

240

# Read df_results_final_3 and df_results_final_2
# df_results_final_3 = pd.read_csv('df_results_final_3.csv')
# df_results_final_2 = pd.read_csv('df_results_final_2.csv')

df = df_droped_001

12. Ajuste de Hiperparâmetros¶

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import loguniform, randint
from skopt import BayesSearchCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from xgboost import XGBClassifier
np.int = int

# Define the dataframe 'df', features, and target
# 'label' is the target column
features =  df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
features = features.sample(frac=0.01, random_state=42)
target = target.sample(frac=0.01, random_state=42)


X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('minmax_scaler', MinMaxScaler((0, 1))),
    ('classifier', XGBClassifier())
])

# Grid Search
param_grid = {
    "classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
    "classifier__n_estimators": [100, 300, 600, 800, 1000],
    "classifier__max_depth": [4, 20, 100, 250, 400]
}

reg_grid = GridSearchCV(pipe,
                        param_grid=param_grid,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc'
                        )

model_grid = reg_grid.fit(X_train, y_train)

# Random Search
n_iter = 70

param_rand = {
    "classifier__learning_rate": loguniform(1e-4, 0.1),
    "classifier__n_estimators": randint(100, 1000),
    "classifier__max_depth": randint(4, 400)
}

reg_rand = RandomizedSearchCV(pipe,
                               param_distributions=param_rand,
                               n_iter=n_iter,
                               cv=5,
                               n_jobs=8,
                               scoring='roc_auc',
                               random_state=123)

model_rand = reg_rand.fit(X_train, y_train)

# Bayesian Search
n_iter = 70

param_bay = {
    "classifier__learning_rate": (0.0001, 0.1, "log-uniform"),
    "classifier__n_estimators": (100, 1000),
    "classifier__max_depth": (4, 400)
}

reg_bay = BayesSearchCV(estimator=pipe,
                        search_spaces=param_bay,
                        n_iter=n_iter,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc',
                        random_state=123)


model_bay = reg_bay.fit(X_train, y_train)

# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
    y_pred_prob = model.predict_proba(X_test)[:, 1]
    metrics_df = calculate_classification_metrics_one_class(y_test, y_pred_prob)
    return metrics_df

# Evaluate models on the test set
metrics_grid = evaluate_model(model_grid, X_test, y_test)
metrics_rand = evaluate_model(model_rand, X_test, y_test)
metrics_bay = evaluate_model(model_bay, X_test, y_test)

# Print the results
print("Grid Search Metrics:")
print(metrics_grid)

print("\nRandom Search Metrics:")
print(metrics_rand)

print("\nBayesian Search Metrics:")
print(metrics_bay)

# Extract the parameter of interest for comparison
param = 'param_classifier__learning_rate'
grid = model_grid.cv_results_[param]
rand = model_rand.cv_results_[param]
bay = model_bay.cv_results_[param]

# Plot the comparison
fig = plt.figure(figsize=(15, 7))
ax = plt.gca()
ax.scatter(np.arange(len(grid)), grid, c='g', s=20, label='grid');
ax.scatter(np.arange(len(rand)), rand, c='r', s=20, label='random');
ax.scatter(np.arange(len(bay)), bay, c='b', s=20, label='bayesian');
ax.set_yscale('log')

plt.legend()
plt.title(param)
plt.show()

Grid Search Metrics:
     Class  TP  TN  FP  FN  Sensitivity_TPR  Specificity   AUC_ROC   AUC_PRC  \
0  Class 1  37   0   1   0              1.0          0.0  0.702703  0.041667   

       Gini        KS  MCC  Kappa  Accuracy  Balanced_Accuracy   F1  \
0  0.405405  0.702703  0.0    0.0  0.973684                0.5  0.0   

   F1_Weighted  Precision  Recall  
0     0.960702        0.0     0.0  

Random Search Metrics:
     Class  TP  TN  FP  FN  Sensitivity_TPR  Specificity   AUC_ROC  AUC_PRC  \
0  Class 1  36   1   1   0              1.0          0.5  0.756757     0.05   

       Gini        KS       MCC     Kappa  Accuracy  Balanced_Accuracy   F1  \
0  0.513514  0.756757 -0.027027 -0.027027  0.947368           0.486486  0.0   

   F1_Weighted  Precision  Recall  
0     0.947368        0.0     0.0  

Bayesian Search Metrics:
     Class  TP  TN  FP  FN  Sensitivity_TPR  Specificity   AUC_ROC  AUC_PRC  \
0  Class 1  36   1   1   0              1.0          0.5  0.756757     0.05   

       Gini        KS       MCC     Kappa  Accuracy  Balanced_Accuracy   F1  \
0  0.513514  0.756757 -0.027027 -0.027027  0.947368           0.486486  0.0   

   F1_Weighted  Precision  Recall  
0     0.947368        0.0     0.0

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import loguniform, randint
from skopt import BayesSearchCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from xgboost import XGBClassifier
np.int = int



# Grid Search
param_grid = {
    "classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
    "classifier__n_estimators": [100, 300, 600, 800, 1000],
    "classifier__max_depth": [4, 20, 100, 250, 400]
}

reg_grid = GridSearchCV(pipe,
                        param_grid=param_grid,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc'
                        )

model_grid = reg_grid.fit(X_train, y_train)

# Random Search
n_iter = 70

param_rand = {
    "classifier__learning_rate": loguniform(1e-4, 0.1),
    "classifier__n_estimators": randint(100, 1000),
    "classifier__max_depth": randint(4, 400)
}

reg_rand = RandomizedSearchCV(pipe,
                               param_distributions=param_rand,
                               n_iter=n_iter,
                               cv=5,
                               n_jobs=8,
                               scoring='roc_auc',
                               random_state=123)

model_rand = reg_rand.fit(X_train, y_train)

# Bayesian Search
n_iter = 70

param_bay = {
    "classifier__learning_rate": (0.0001, 0.1, "log-uniform"),
    "classifier__n_estimators": (100, 1000),
    "classifier__max_depth": (4, 400)
}

reg_bay = BayesSearchCV(estimator=pipe,
                        search_spaces=param_bay,
                        n_iter=n_iter,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc',
                        random_state=123)

model_bay = reg_bay.fit(X_train, y_train)

# Extract the parameter of interest for comparison
param = 'param_classifier__learning_rate'
grid = model_grid.cv_results_[param]
rand = model_rand.cv_results_[param]
bay = model_bay.cv_results_[param]

# Plot the comparison
fig = plt.figure(figsize=(15, 7))
ax = plt.gca()
ax.scatter(np.arange(len(grid)), grid, c='g', s=20, label='grid');
ax.scatter(np.arange(len(rand)), rand, c='r', s=20, label='random');
ax.scatter(np.arange(len(bay)), bay, c='b', s=20, label='bayesian');
ax.set_yscale('log')

plt.legend()
plt.title(param)
plt.show()

# Using XGBoost. Hyperparameters are tuned using Bayesian Search, grid search, and random search
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, roc_curve, auc
from xgboost import XGBClassifier
import optuna

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
features = features.sample(frac=0.01, random_state=42)
target = target.sample(frac=0.01, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('minmax_scaler', MinMaxScaler((0, 1))),
    ('classifier', XGBClassifier())
])

# Grid Search
param_grid = {
    "classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
    "classifier__n_estimators": [100, 300, 600, 800, 1000],
    "classifier__max_depth": [4, 20, 100, 250, 400]
}

reg_grid = GridSearchCV(pipe,
                        param_grid=param_grid,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc'
                        )

model_grid = reg_grid.fit(X_train, y_train)

# Random Search
n_iter_rand = 70

param_rand = {
    "classifier__learning_rate": np.logspace(-4, -1, num=1000),
    "classifier__n_estimators": np.random.randint(100, 1000, size=n_iter_rand),
    "classifier__max_depth": np.random.randint(4, 400, size=n_iter_rand)
}

reg_rand = RandomizedSearchCV(pipe,
                               param_distributions=param_rand,
                               n_iter=n_iter_rand,
                               cv=5,
                               n_jobs=8,
                               scoring='roc_auc',
                               random_state=123)

model_rand = reg_rand.fit(X_train, y_train)

# Optuna Search
n_iter_optuna = 70

def objective(trial):
    params = {
        "learning_rate": trial.suggest_loguniform("learning_rate", 1e-4, 0.1),
        "n_estimators": trial.suggest_int("n_estimators", 100, 1000),
        "max_depth": trial.suggest_int("max_depth", 4, 400)
    }

    # Set the classifier parameters in the pipeline
    pipe.set_params(classifier__learning_rate=params["learning_rate"],
                   classifier__n_estimators=params["n_estimators"],
                   classifier__max_depth=params["max_depth"])

    # Fit the model and calculate ROC-AUC
    model = pipe.fit(X_train, y_train)
    y_pred_prob = model.predict_proba(X_test)[:, 1]
    roc_auc = roc_auc_score(y_test, y_pred_prob)

    return roc_auc

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=n_iter_optuna)

# Extract results from studies
results_grid = pd.DataFrame(model_grid.cv_results_)
results_grid['iteration'] = range(1, len(results_grid) + 1)  # Add iteration column

results_rand = pd.DataFrame(model_rand.cv_results_)
results_rand['iteration'] = range(1, len(results_rand) + 1)  # Add iteration column

results_optuna = study.trials_dataframe()
results_optuna['iteration'] = range(1, len(results_optuna) + 1)

# Get the best parameters from each optimization
best_params_grid = model_grid.best_params_
best_params_rand = model_rand.best_params_
best_params_optuna = study.best_params

# Update the pipeline with the best parameters
pipe.set_params(classifier__learning_rate=best_params_optuna["learning_rate"],
                classifier__n_estimators=best_params_optuna["n_estimators"],
                classifier__max_depth=best_params_optuna["max_depth"])

# Fit the final model
final_model_optuna = pipe.fit(X_train, y_train)

# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
    y_pred_prob = model.predict_proba(X_test)[:, 1]
    roc_auc = roc_auc_score(y_test, y_pred_prob)
    balanced_acc = balanced_accuracy_score(y_test, (y_pred_prob >= 0.5).astype(int))
    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
    ks = np.max(tpr - fpr)
    return roc_auc, balanced_acc, ks

# Evaluate models on the test set
roc_auc_grid, balanced_acc_grid, ks_grid = evaluate_model(model_grid, X_test, y_test)
roc_auc_rand, balanced_acc_rand, ks_rand = evaluate_model(model_rand, X_test, y_test)
roc_auc_optuna, balanced_acc_optuna, ks_optuna = evaluate_model(final_model_optuna, X_test, y_test)


# Scatter plot for Balanced Accuracy
fig, axes = plt.subplots(3, 1, figsize=(15, 15))

# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green', ax=axes[0])

# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red', ax=axes[1])

# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue', ax=axes[2])

plt.tight_layout()
plt.legend()
plt.show()

[I 2024-01-07 20:22:01,493] A new study created in memory with name: no-name-b4d09e12-9b4c-4355-84be-95a9e4a63b55
[I 2024-01-07 20:22:02,190] Trial 0 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.025965778968787823, 'n_estimators': 654, 'max_depth': 13}. Best is trial 0 with value: 0.6486486486486487.
[I 2024-01-07 20:22:03,449] Trial 1 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.000671343015223328, 'n_estimators': 620, 'max_depth': 246}. Best is trial 0 with value: 0.6486486486486487.
[I 2024-01-07 20:22:05,636] Trial 2 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0003351676721857623, 'n_estimators': 725, 'max_depth': 54}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:06,825] Trial 3 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.004892589507419087, 'n_estimators': 536, 'max_depth': 207}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:07,836] Trial 4 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0001075416374785266, 'n_estimators': 535, 'max_depth': 289}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:08,393] Trial 5 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.014583763534570878, 'n_estimators': 360, 'max_depth': 9}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:08,667] Trial 6 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.001991124210189203, 'n_estimators': 122, 'max_depth': 102}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:09,626] Trial 7 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0003363310632254498, 'n_estimators': 471, 'max_depth': 227}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:10,155] Trial 8 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.00011136028481532918, 'n_estimators': 272, 'max_depth': 219}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:10,713] Trial 9 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.09725144251556718, 'n_estimators': 823, 'max_depth': 233}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:11,891] Trial 10 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.012822952519384409, 'n_estimators': 999, 'max_depth': 377}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:12,973] Trial 11 finished with value: 0.7027027027027027 and parameters: {'learning_rate': 0.002146678639934428, 'n_estimators': 316, 'max_depth': 21}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:14,014] Trial 12 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.013949002702370919, 'n_estimators': 806, 'max_depth': 102}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:14,840] Trial 13 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0006043500352381994, 'n_estimators': 402, 'max_depth': 98}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:15,429] Trial 14 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.06960143328013517, 'n_estimators': 744, 'max_depth': 42}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:15,855] Trial 15 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.005278360731684821, 'n_estimators': 195, 'max_depth': 153}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:16,736] Trial 16 finished with value: 0.5675675675675675 and parameters: {'learning_rate': 0.02910461336786791, 'n_estimators': 963, 'max_depth': 63}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:17,528] Trial 17 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0009852406514023015, 'n_estimators': 377, 'max_depth': 150}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:18,566] Trial 18 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.008310746381248431, 'n_estimators': 698, 'max_depth': 155}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:20,171] Trial 19 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.010628671253220482, 'n_estimators': 880, 'max_depth': 328}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:21,175] Trial 20 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.042370721334982, 'n_estimators': 625, 'max_depth': 157}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:22,335] Trial 21 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.01022403598345688, 'n_estimators': 892, 'max_depth': 365}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:23,937] Trial 22 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.007625021892927763, 'n_estimators': 816, 'max_depth': 325}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:24,894] Trial 23 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.01943759300165951, 'n_estimators': 893, 'max_depth': 280}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:26,320] Trial 24 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0032695237679415635, 'n_estimators': 686, 'max_depth': 328}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:27,367] Trial 25 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.007036535264437527, 'n_estimators': 459, 'max_depth': 178}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:28,092] Trial 26 finished with value: 0.4864864864864865 and parameters: {'learning_rate': 0.05272574819387728, 'n_estimators': 892, 'max_depth': 397}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:29,157] Trial 27 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0031373974582856764, 'n_estimators': 576, 'max_depth': 276}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:30,502] Trial 28 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.019016736749388616, 'n_estimators': 760, 'max_depth': 124}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:31,193] Trial 29 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.03049279338828089, 'n_estimators': 696, 'max_depth': 4}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:31,692] Trial 30 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.008838040174117432, 'n_estimators': 259, 'max_depth': 188}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:32,869] Trial 31 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.013432282847501318, 'n_estimators': 891, 'max_depth': 345}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:34,376] Trial 32 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.009513091127698128, 'n_estimators': 873, 'max_depth': 350}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:35,362] Trial 33 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.01965196926100892, 'n_estimators': 948, 'max_depth': 306}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:36,885] Trial 34 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.004832096458219818, 'n_estimators': 783, 'max_depth': 251}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:38,287] Trial 35 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.003131501073417488, 'n_estimators': 651, 'max_depth': 73}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:39,926] Trial 36 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0056938408858875226, 'n_estimators': 596, 'max_depth': 257}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:41,956] Trial 37 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0013323534187582912, 'n_estimators': 848, 'max_depth': 355}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:42,544] Trial 38 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.03675698558198616, 'n_estimators': 491, 'max_depth': 310}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:43,346] Trial 39 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.021581861970830285, 'n_estimators': 698, 'max_depth': 391}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:43,600] Trial 40 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.010769989058309089, 'n_estimators': 105, 'max_depth': 29}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:44,966] Trial 41 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.00984462684255336, 'n_estimators': 863, 'max_depth': 362}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:46,621] Trial 42 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0042665509632374855, 'n_estimators': 852, 'max_depth': 342}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:48,164] Trial 43 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.016096497248778596, 'n_estimators': 950, 'max_depth': 372}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:49,510] Trial 44 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.00795611515849533, 'n_estimators': 990, 'max_depth': 305}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:50,980] Trial 45 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.011619927770037624, 'n_estimators': 739, 'max_depth': 206}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:51,783] Trial 46 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.02541243937950875, 'n_estimators': 770, 'max_depth': 209}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:52,703] Trial 47 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.014154343606122883, 'n_estimators': 717, 'max_depth': 127}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:53,722] Trial 48 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0020671816761863703, 'n_estimators': 516, 'max_depth': 72}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:55,226] Trial 49 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.00646344480543513, 'n_estimators': 660, 'max_depth': 233}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:56,077] Trial 50 finished with value: 0.7027027027027027 and parameters: {'learning_rate': 0.004268416695500814, 'n_estimators': 154, 'max_depth': 183}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:57,156] Trial 51 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.012280808994455104, 'n_estimators': 851, 'max_depth': 378}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:58,674] Trial 52 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.010402415933777507, 'n_estimators': 790, 'max_depth': 329}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:59,783] Trial 53 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.009917071374354146, 'n_estimators': 798, 'max_depth': 354}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:01,205] Trial 54 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.01614759389652774, 'n_estimators': 758, 'max_depth': 120}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:02,779] Trial 55 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.0064317297312761635, 'n_estimators': 929, 'max_depth': 257}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:05,135] Trial 56 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0002056946747683765, 'n_estimators': 825, 'max_depth': 84}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:05,862] Trial 57 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.008527330691775727, 'n_estimators': 410, 'max_depth': 316}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:06,495] Trial 58 finished with value: 0.4864864864864865 and parameters: {'learning_rate': 0.052042202286553964, 'n_estimators': 729, 'max_depth': 292}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:07,003] Trial 59 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.025647802078437684, 'n_estimators': 355, 'max_depth': 386}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:08,314] Trial 60 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0040889314649698695, 'n_estimators': 627, 'max_depth': 46}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:09,716] Trial 61 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.011357254937295789, 'n_estimators': 921, 'max_depth': 331}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:10,885] Trial 62 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.01644331011111973, 'n_estimators': 870, 'max_depth': 361}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:12,338] Trial 63 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.009191924000294898, 'n_estimators': 826, 'max_depth': 290}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:13,757] Trial 64 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.006223056711545702, 'n_estimators': 786, 'max_depth': 339}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:14,518] Trial 65 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0026098869932645917, 'n_estimators': 241, 'max_depth': 164}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:15,526] Trial 66 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.011487178978641032, 'n_estimators': 741, 'max_depth': 272}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:16,275] Trial 67 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.03170790206833891, 'n_estimators': 731, 'max_depth': 241}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:17,073] Trial 68 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.02106495154650321, 'n_estimators': 685, 'max_depth': 218}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:17,890] Trial 69 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.01304621508071494, 'n_estimators': 565, 'max_depth': 273}. Best is trial 32 with value: 0.8108108108108107.

# Plot the comparison using scatter plots
# Determine fig size
fig = plt.figure(figsize=(15, 7))

# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green')
# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red')
# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue')

# Set y scale between 0.5 and 0.7
plt.ylim(0.5, 0.65)

plt.tight_layout()
plt.legend()
plt.show()

###################### Using XGBoost. Hyperparameters are tuned using Bayesian Search, grid search, and random search
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, roc_curve, auc
from xgboost import XGBClassifier
import optuna

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
#features = features.sample(frac=1, random_state=42)
#target = target.sample(frac=1, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('minmax_scaler', MinMaxScaler((0, 1))),
    ('classifier', XGBClassifier())
])

# Grid Search
param_grid = {
    "classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
    "classifier__n_estimators": [100, 300, 600, 800, 1000],
    "classifier__max_depth": [4, 20, 100, 250, 400]
}

reg_grid = GridSearchCV(pipe,
                        param_grid=param_grid,
                        cv=5,
                        n_jobs=8,
                        scoring='roc_auc'
                        )

model_grid = reg_grid.fit(X_train, y_train)

# Random Search
n_iter_rand = 70

param_rand = {
    "classifier__learning_rate": np.logspace(-4, -1, num=1000),
    "classifier__n_estimators": np.random.randint(100, 1000, size=n_iter_rand),
    "classifier__max_depth": np.random.randint(4, 400, size=n_iter_rand)
}

reg_rand = RandomizedSearchCV(pipe,
                               param_distributions=param_rand,
                               n_iter=n_iter_rand,
                               cv=5,
                               n_jobs=8,
                               scoring='roc_auc',
                               random_state=123)

model_rand = reg_rand.fit(X_train, y_train)

# Optuna Search
n_iter_optuna = 70

def objective(trial):
    params = {
        "learning_rate": trial.suggest_loguniform("learning_rate", 1e-4, 0.1),
        "n_estimators": trial.suggest_int("n_estimators", 100, 1000),
        "max_depth": trial.suggest_int("max_depth", 4, 400)
    }

    # Set the classifier parameters in the pipeline
    pipe.set_params(classifier__learning_rate=params["learning_rate"],
                   classifier__n_estimators=params["n_estimators"],
                   classifier__max_depth=params["max_depth"])

    # Fit the model and calculate ROC-AUC
    model = pipe.fit(X_train, y_train)
    y_pred_prob = model.predict_proba(X_test)[:, 1]
    roc_auc = roc_auc_score(y_test, y_pred_prob)

    return roc_auc

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=n_iter_optuna)

# Extract results from studies
results_grid = pd.DataFrame(model_grid.cv_results_)
results_grid['iteration'] = range(1, len(results_grid) + 1)  # Add iteration column

results_rand = pd.DataFrame(model_rand.cv_results_)
results_rand['iteration'] = range(1, len(results_rand) + 1)  # Add iteration column

results_optuna = study.trials_dataframe()
results_optuna['iteration'] = range(1, len(results_optuna) + 1)

# Get the best parameters from each optimization
best_params_grid = model_grid.best_params_
best_params_rand = model_rand.best_params_
best_params_optuna = study.best_params

# Update the pipeline with the best parameters
pipe.set_params(classifier__learning_rate=best_params_optuna["learning_rate"],
                classifier__n_estimators=best_params_optuna["n_estimators"],
                classifier__max_depth=best_params_optuna["max_depth"])

# Fit the final model
final_model_optuna = pipe.fit(X_train, y_train)

# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
    y_pred_prob = model.predict_proba(X_test)[:, 1]
    roc_auc = roc_auc_score(y_test, y_pred_prob)
    balanced_acc = balanced_accuracy_score(y_test, (y_pred_prob >= 0.5).astype(int))
    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
    ks = np.max(tpr - fpr)
    return roc_auc, balanced_acc, ks

# Evaluate models on the test set
roc_auc_grid, balanced_acc_grid, ks_grid = evaluate_model(model_grid, X_test, y_test)
roc_auc_rand, balanced_acc_rand, ks_rand = evaluate_model(model_rand, X_test, y_test)
roc_auc_optuna, balanced_acc_optuna, ks_optuna = evaluate_model(final_model_optuna, X_test, y_test)


# Scatter plot for Balanced Accuracy
fig, axes = plt.subplots(3, 1, figsize=(15, 15))

# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green', ax=axes[0])

# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red', ax=axes[1])

# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue', ax=axes[2])

plt.tight_layout()
plt.legend()
plt.show()

# Check the scoring metric used in Grid Search
print("Grid Search Scoring Metric:", reg_grid.scoring)

# Check the scoring metric used in Random Search
print("Random Search Scoring Metric:", reg_rand.scoring)

# Check the scoring metric used in Bayesian Search
print("Bayesian Search Scoring Metric:", reg_bay.scoring)

Grid Search Scoring Metric: roc_auc
Random Search Scoring Metric: roc_auc
Bayesian Search Scoring Metric: roc_auc

# For Grid Search
best_params_grid = model_grid.best_params_
print("Grid Search Best Hyperparameters:", best_params_grid)

# For Random Search
best_params_rand = model_rand.best_params_
print("Random Search Best Hyperparameters:", best_params_rand)

# For Optuna Search
best_params_optuna = study.best_params
print("Optuna Search Best Hyperparameters:", best_params_optuna)

Grid Search Best Hyperparameters: {'classifier__learning_rate': 0.0001, 'classifier__max_depth': 4, 'classifier__n_estimators': 100}
Random Search Best Hyperparameters: {'classifier__n_estimators': 868, 'classifier__max_depth': 326, 'classifier__learning_rate': 0.0016451905877536625}
Optuna Search Best Hyperparameters: {'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131}

study.trials_dataframe().head(20)

13. Interpretação de Modelo¶

# Get feature importances
importances = model.feature_importances_

# Get feature names
feature_names = X_train.columns

# Sort feature importances in descending order
indices = importances.argsort()[::-1]

# Plot the feature importances
# Plot the top 20 feature importances
top_n = 40

plt.figure(figsize=(21, 7))
plt.bar(range(top_n), importances[indices][:top_n])
plt.xticks(range(top_n), feature_names[indices][:top_n], rotation=45)
plt.xlabel('Feature')
plt.ylabel('Importance')
plt.title('Top 20 XGBoost Feature Importance')
plt.show()

14. Implantação de Modelo¶

Configuração do Ambiente de Produção Para configurar o ambiente de produção de forma eficiente, adotamos práticas específicas para garantir a estabilidade e o desempenho do modelo. Utilizamos a ferramenta pip freeze para gerar um arquivo requirements.txt, contendo as versões exatas das bibliotecas utilizadas durante o desenvolvimento. Isso previne possíveis erros e conflitos de versões, assegurando a consistência do ambiente. Além disso, optamos por armazenar o modelo em um arquivo do tipo pickle para facilitar a carga e descarga, proporcionando maior agilidade na integração do modelo no ambiente de produção. A utilização de containers, como Docker, é recomendada para garantir a robustez e reprodutibilidade do ambiente em diferentes configurações, facilitando a escalabilidade e a manutenção.

Integração com Sistemas Existentes Na integração do modelo com sistemas existentes, adotamos uma abordagem prática para garantir uma implementação eficaz. Desenvolvemos interfaces customizadas para facilitar a comunicação entre o modelo e outros componentes do sistema, garantindo uma integração suave. Para facilitar a manutenção, utilizamos logs detalhados para monitorar a entrada e saída do modelo, permitindo uma rápida identificação de eventuais problemas. Essa abordagem prática visa minimizar os impactos operacionais e otimizar a contribuição do modelo no contexto mais amplo do ambiente de produção. Optamos por integrar o aplicativo Streamlit com o pipeline de produção. Essa integração visa assegurar que os dados do usuário sejam processados de maneira eficiente e que as previsões sejam geradas em tempo real. O uso do Streamlit proporciona uma interface amigável e interativa para os usuários, tornando a experiência mais acessível e facilitando o acesso às funcionalidades do modelo. Essa abordagem de integração permite uma transição suave do modelo do ambiente de desenvolvimento para um ambiente de produção, garantindo que o modelo treinado seja facilmente acessível e capaz de fornecer previsões em tempo real conforme necessário.

Configuração do Ambiente de Produção
Para configurar o ambiente de produção de forma eficiente, adotamos práticas específicas para garantir a estabilidade e o desempenho do modelo. Utilizamos a ferramenta pip freeze para gerar um arquivo requirements.txt, contendo as versões exatas das bibliotecas utilizadas durante o desenvolvimento. Isso previne possíveis erros e conflitos de versões, assegurando a consistência do ambiente. Além disso, optamos por armazenar o modelo em um arquivo do tipo pickle para facilitar a carga e descarga, proporcionando maior agilidade na integração do modelo no ambiente de produção. A utilização de containers, como Docker, é recomendada para garantir a robustez e reprodutibilidade do ambiente em diferentes configurações, facilitando a escalabilidade e a manutenção.

Integração com Sistemas Existentes:
Na integração do modelo com sistemas existentes, adotamos uma abordagem prática para garantir uma implementação eficaz. Desenvolvemos interfaces customizadas para facilitar a comunicação entre o modelo e outros componentes do sistema, garantindo uma integração suave.
Para facilitar a manutenção, utilizamos logs detalhados para monitorar a entrada e saída do modelo, permitindo uma rápida identificação de eventuais problemas. Essa abordagem prática visa minimizar os impactos operacionais e otimizar a contribuição do modelo no contexto mais amplo do ambiente de produção.
Optamos por integrar o aplicativo Streamlit com o pipeline de produção. Essa integração visa assegurar que os dados do usuário sejam processados de maneira eficiente e que as previsões sejam geradas em tempo real. O uso do Streamlit proporciona uma interface amigável e interativa para os usuários, tornando a experiência mais acessível e facilitando o acesso às funcionalidades do modelo.
Essa abordagem de integração permite uma transição suave do modelo do ambiente de desenvolvimento para um ambiente de produção, garantindo que o modelo treinado seja facilmente acessível e capaz de fornecer previsões em tempo real conforme necessário.

15. Monitoramento e Manutenção de Modelo¶

A manutenção e monitoramento contínuo do modelo são aspectos críticos para garantir sua eficácia ao longo do tempo. Mesmo que o modelo ainda não tenha sido oficialmente implantado em produção, é fundamental antecipar práticas que garantam a robustez e relevância contínua. Aqui estão algumas sugestões teóricas para proceder:

Verificação de Data Drift.
Suposições e Mudanças de Comportamento.
Manutenção Proativa.
Documentação e Comunicação.
Estabilidade em função do tempo. (Importante e prático)

Continuous model maintenance and monitoring are critical aspects to ensure its effectiveness over time. Even though the model has not yet been officially deployed in production, it is essential to anticipate practices that guarantee robustness and continuous relevance. Here are some theoretical suggestions to proceed:

Data Drift Verification.
Assumptions and Behavior Changes.
Proactive Maintenance.
Documentation and Communication.
Stability over time. (Important and practical)

Considerações Finais¶

Finalmente, é relevante ressaltar que o processo de modelagem é intrinsecamente complexo e não segue uma sequência rígida de etapas. Em vez disso, envolve a formulação e teste contínuo de hipóteses, ajustes e refinamentos iterativos. Esse ciclo de melhoria contínua é uma parte essencial do acompanhamento do modelo ao longo de sua vida útil, visando garantir não apenas a confiabilidade, mas também a otimização contínua dos resultados enquanto o modelo está em produção. A flexibilidade para se adaptar a novos dados, condições de mercado e requisitos de negócios é fundamental para a eficácia a longo prazo do modelo de risco de crédito.

pwd

'/content'

!jupyter contrib nbextension install --user

Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Scripts\jupyter-contrib.EXE\__main__.py", line 7, in <module>
  File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\jupyter_core\application.py", line 280, in launch_instance
    super().launch_instance(argv=argv, **kwargs)
  File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\traitlets\config\application.py", line 1051, in launch_instance
    app = cls.instance(**kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\traitlets\config\configurable.py", line 583, in instance
    inst = cls(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_core\application.py", line 27, in __init__
    self._refresh_subcommands()
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_core\application.py", line 43, in _refresh_subcommands
    get_subcommands_dict = entrypoint.load()
                           ^^^^^^^^^^^^^^^^^
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pkg_resources\__init__.py", line 2516, in load
    return self.resolve()
           ^^^^^^^^^^^^^^
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pkg_resources\__init__.py", line 2522, in resolve
    module = __import__(self.module_name, fromlist=['__name__'], level=0)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_nbextensions\__init__.py", line 5, in <module>
    import jupyter_nbextensions_configurator
  File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_nbextensions_configurator\__init__.py", line 18, in <module>
    from notebook.base.handlers import APIHandler, IPythonHandler
ModuleNotFoundError: No module named 'notebook.base'

df = pd.read_csv(r'C:\Temp\Codes\df_droped_001.csv')

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier

import os
import json

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the hyperparameters for each model
hyperparameters = {
    'Logistic Regression': {'C': [0.1, 1, 10]},
    'k-Nearest Neighbors (k-NN)': {'n_neighbors': [3, 5, 7]},
    'Support Vector Machines (SVM)': {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']},
    'Neural Networks': {'hidden_layer_sizes': [(50,), (100,), (50, 50)]},
    'Naive Bayes': {},
    'Decision Trees': {'max_depth': [None, 10, 20]},
    'Random Forest': {'n_estimators': [50, 100, 200]},
    'Gradient Boosting Machines (GBM)': {'n_estimators': [50, 100, 200]},
    'XGBoost': {'class_weight=': [None, 'balanced'], 'n_estimators': [50, 100, 200]},
    'LightGBM': {'n_estimators': [50, 100, 200]},
    'CatBoost': {'iterations': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2]},
    'HistGradientBoostingClassifier': {'class_weight=': ['balanced', [None]], 'max_iter': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2]}
}

# Define the imbalanced data handling techniques
sampling_techniques = {
    'None': None,
    'Oversampling': RandomOverSampler(),
    'Undersampling': RandomUnderSampler(),
    #'Hybrid Sampling (SMOTEENN)': SMOTEENN(), #Does not accept NaN values
    #'Hybrid Sampling (SMOTETomek)': SMOTETomek()
}

# Define the imputation techniques
imputation_techniques = {
    #'XGBoost': None,
    'None': None,
    'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
    #'Mean Imputation': SimpleImputer(strategy='mean'),
    #'Median Imputation': SimpleImputer(strategy='median')
}

# Define a function to calculate KS and Gini
# def calculate_ks_gini(y_true, y_prob):
#     fpr, tpr, thresholds = roc_curve(y_true, y_prob)
#     ks = np.max(tpr - fpr)
#     gini = 2 * auc(fpr, tpr) - 1
#     return ks, gini

# Create a list to store the results
results_2 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [
    # ('Logistic Regression', LogisticRegression()),
     ('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
    # ('Support Vector Machines (SVM)', SVC(probability=True)),
    # ('Neural Networks', MLPClassifier()),
    # ('Naive Bayes', GaussianNB()),
    # ('Decision Trees', DecisionTreeClassifier()),
    # ('Random Forest', RandomForestClassifier()),
    # ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
     ('XGBoost', XGBClassifier()),
    # ('LightGBM', LGBMClassifier()),
    # ('CatBoost', CatBoostClassifier()),
    # ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())



]:
    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():
        # Iterate through each imputation technique
        for imputation_name, imputation_technique in imputation_techniques.items():
            # Create a new dataframe to store the results for this model, technique combination
            df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])


            # Perform k-fold cross-validation
            for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                # Apply imputation technique on the train and test sets, if imputation_name is not None
                if imputation_name is not None and imputation_technique is not None:
                    print(imputation_name)
                    X_train_imputed = imputation_technique.fit_transform(X_train)
                    X_test_imputed = imputation_technique.transform(X_test)
                else:
                    print(imputation_name)
                    if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                        X_train_imputed, X_test_imputed = X_train, X_test
                    else:
                        break

                # Apply the imbalanced data handling technique on the train set
                if sampling_technique is not None:
                    X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
                else:
                    X_train_resampled, y_train_resampled = X_train_imputed, y_train


                # Train the model on the resampled train set
                model.fit(X_train_resampled, y_train_resampled)

                # Make predictions on the test set
                y_pred = model.predict(X_test_imputed)
                y_prob = model.predict_proba(X_test_imputed)[:, 1]

                # Calculate the evaluation metrics
                metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                # Set 'Imputation Technique' based on the specified conditions
                if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                    imputation_name = 'None'
                else:
                    imputation_name = imputation_name

                #imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name


                # Create a new DataFrame for each iteration
                df_result_2 = pd.DataFrame({
                    'Algorithm': model_name,
                    'Hyperparameters': [model.get_params()],
                    'Imputation Technique': imputation_name,
                    'Imbalance Class Technique': sampling_name,
                    'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
                    **metrics_df  # Unpack the metrics_df columns
                })

                # Append the result to the list
                results_2.append(df_result_2)

# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
    df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_2.csv", index=False)

# Display the final DataFrame
display(df_results_final_2)

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier

import os
import json

# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']

# Define the hyperparameters for each model
hyperparameters = {
    'XGBoost': { 'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131},

}

# Define the imbalanced data handling techniques
sampling_techniques = {
#    'None': None,
#     'Oversampling': RandomOverSampler(),
     'Undersampling': RandomUnderSampler(),
#     #'Hybrid Sampling (SMOTEENN)': SMOTEENN(), #Does not accept NaN values
#     #'Hybrid Sampling (SMOTETomek)': SMOTETomek()
 }

# Define the imputation techniques
imputation_techniques = {
    #'XGBoost': None,
    'None': None,
    # 'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
    #'Mean Imputation': SimpleImputer(strategy='mean'),
    #'Median Imputation': SimpleImputer(strategy='median')
}

# Define a function to calculate KS and Gini
# def calculate_ks_gini(y_true, y_prob):
#     fpr, tpr, thresholds = roc_curve(y_true, y_prob)
#     ks = np.max(tpr - fpr)
#     gini = 2 * auc(fpr, tpr) - 1
#     return ks, gini

# Create a list to store the results
results_2 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [

     ('XGBoost', XGBClassifier()),


]:
    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():
        # Iterate through each imputation technique
        for imputation_name, imputation_technique in imputation_techniques.items():
            # Create a new dataframe to store the results for this model, technique combination
            df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])


            # Perform k-fold cross-validation
            for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                # Apply imputation technique on the train and test sets, if imputation_name is not None
                if imputation_name is not None and imputation_technique is not None:
                    print(imputation_name)
                    X_train_imputed = imputation_technique.fit_transform(X_train)
                    X_test_imputed = imputation_technique.transform(X_test)
                else:
                    print(imputation_name)
                    if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                        X_train_imputed, X_test_imputed = X_train, X_test
                    else:
                        break

                # Apply the imbalanced data handling technique on the train set
                if sampling_technique is not None:
                    X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
                else:
                    X_train_resampled, y_train_resampled = X_train_imputed, y_train


                # Train the model on the resampled train set
                model.fit(X_train_resampled, y_train_resampled)

                # Make predictions on the test set
                y_pred = model.predict(X_test_imputed)
                y_prob = model.predict_proba(X_test_imputed)[:, 1]

                # Calculate the evaluation metrics
                metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                # Set 'Imputation Technique' based on the specified conditions
                if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
                    imputation_name = 'None'
                else:
                    imputation_name = imputation_name

                #imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name


                # Create a new DataFrame for each iteration
                df_result_2 = pd.DataFrame({
                    'Algorithm': model_name,
                    'Hyperparameters': [model.get_params()],
                    'Imputation Technique': imputation_name,
                    'Imbalance Class Technique': sampling_name,
                    'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
                    **metrics_df  # Unpack the metrics_df columns
                })

                # Append the result to the list
                results_2.append(df_result_2)

# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
    df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_2f.csv", index=False)

# Display the final DataFrame
display(df_results_final_2)

None
None
None
None
None

df_results_final_2['AUC_ROC']

0    0.681388
1    0.675467
2    0.664150
3    0.637342
4    0.697207
Name: AUC_ROC, dtype: float64

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import roc_curve, auc


# Define the dataframe 'df', features, and target
features = df.columns[1:479]
target = df['label']

# Define the hyperparameters for each model
hyperparameters = {
    'XGBoost': {'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131},
}

# Define the imbalanced data handling techniques
sampling_techniques = {
    'Undersampling': RandomUnderSampler(),
}

# Define the imputation techniques
imputation_techniques = {
    'None': None,
}

# Create a list to store the results
results_2 = []

# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)

# Iterate through each model
for model_name, model in [('XGBoost', XGBClassifier(**hyperparameters['XGBoost']))]:

    # Iterate through each imbalanced data handling technique
    for sampling_name, sampling_technique in sampling_techniques.items():
        # Iterate through each imputation technique
        for imputation_name, imputation_technique in imputation_techniques.items():
            # Create a new dataframe to store the results for this model, technique combination
            df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])

            # Perform k-fold cross-validation
            for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
                X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
                y_train, y_test = target.iloc[train_index], target.iloc[test_index]

                # Apply Standard Scaler followed by MinMaxScaler[0,1]
                scaler = StandardScaler()
                X_train_scaled = scaler.fit_transform(X_train)
                X_test_scaled = scaler.transform(X_test)

                minmax_scaler = MinMaxScaler(feature_range=(0, 1))
                X_train_scaled_minmax = minmax_scaler.fit_transform(X_train_scaled)
                X_test_scaled_minmax = minmax_scaler.transform(X_test_scaled)

                # Apply the imbalanced data handling technique on the train set
                if sampling_technique is not None:
                    X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_scaled_minmax, y_train)
                else:
                    X_train_resampled, y_train_resampled = X_train_scaled_minmax, y_train

                # Train the model on the resampled train set
                model.fit(X_train_resampled, y_train_resampled)

                # Make predictions on the test set
                y_prob = model.predict_proba(X_test_scaled_minmax)[:, 1]

                # Calculate the evaluation metrics
                metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)

                # Create a new DataFrame for each iteration
                df_result_2 = pd.DataFrame({
                    'Algorithm': model_name,
                    'Hyperparameters': [model.get_params()],
                    'Imputation Technique': imputation_name,
                    'Imbalance Class Technique': sampling_name,
                    'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
                    **metrics_df  # Unpack the metrics_df columns
                })

                # Append the result to the list
                results_2.append(df_result_2)

# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)

# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
    df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
    df_model.to_csv(f"{model_unique_code}_2ff.csv", index=False)

# Display the final DataFrame
display(df_results_final_2)

df_results_final_2['AUC_ROC']

0    0.681903
1    0.677183
2    0.680051
3    0.656667
4    0.691129
Name: AUC_ROC, dtype: float64

import xgboost as xgb
import matplotlib.pyplot as plt



# Plot feature importance
xgb.plot_importance(model, max_num_features=20)  # Adjust max_num_features as needed
plt.show()

%pip install Boruta

Requirement already satisfied: Boruta in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (0.3)
Requirement already satisfied: numpy>=1.10.4 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.26.2)
Requirement already satisfied: scikit-learn>=0.17.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.3.2)
Requirement already satisfied: scipy>=0.17.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.11.4)
Requirement already satisfied: joblib>=1.1.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from scikit-learn>=0.17.1->Boruta) (1.3.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from scikit-learn>=0.17.1->Boruta) (3.2.0)
Note: you may need to restart the kernel to use updated packages.

from boruta import BorutaPy
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

np.int = int
np.float = float
np.bool = bool

X = df.iloc[:, 1:-1]
y = df['label']

X = X.fillna(0)

# Define algo
rf = XGBClassifier()

# Define Boruta feature selection method
feat_selector = BorutaPy(rf, n_estimators='auto',verbose=2, random_state=1)

X_numpy = X.values
y = y.ravel()
# Find all relevant features
feat_selector.fit(X_numpy, y)

# Check selected features
selected_features = feat_selector.support_

# Check ranking of features
feature_ranking = feat_selector.ranking_

# Call transform() on X to filter it down to selected features
X_filtered = feat_selector.transform(X_numpy)

# Print selected features
print("Selected Features:")
for i, selected in enumerate(selected_features):
    if selected:
        print(f'Feature {i + 1}')

# Print feature ranking
print("\nFeature Ranking:")
for i, rank in enumerate(feature_ranking):
    print(f'Feature {i + 1}: Rank {rank}')

selected_features_indices = np.where(feat_selector.support_)[0]
# Print selected features
print("\n   >Selected Features:")
for feature_idx in selected_features_indices:
    print(f'Feature {feature_idx + 1}')

feature_names = X.columns.tolist()

print("\n   >Selected Features:")

for feature_idx in selected_features_indices:
    print(f'{feature_names[feature_idx]}')

# Define the model (e.g., RandomForestRegressor)
model = XGBClassifier(random_state=0)

# Train the model using selected features
model.fit(X_filtered, y)

# Predict the target variable using the selected features
y_pred = model.predict(X_filtered)

# Calculate MSE
mse = mean_squared_error(y, y_pred)

# Display MSE
print(f'Mean Squared Error (MSE): {mse}')

selected_feature_names = [feature_names[idx] for idx in selected_features_indices]
print(selected_feature_names)

Iteration: 	1 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	2 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	3 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	4 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	5 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	6 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	7 / 100
Confirmed: 	0
Tentative: 	479
Rejected: 	0
Iteration: 	8 / 100
Confirmed: 	0
Tentative: 	33
Rejected: 	446
Iteration: 	9 / 100
Confirmed: 	0
Tentative: 	33
Rejected: 	446
Iteration: 	10 / 100
Confirmed: 	0
Tentative: 	33
Rejected: 	446
Iteration: 	11 / 100
Confirmed: 	0
Tentative: 	33
Rejected: 	446
Iteration: 	12 / 100
Confirmed: 	0
Tentative: 	16
Rejected: 	463
Iteration: 	13 / 100
Confirmed: 	0
Tentative: 	16
Rejected: 	463
Iteration: 	14 / 100
Confirmed: 	0
Tentative: 	16
Rejected: 	463
Iteration: 	15 / 100
Confirmed: 	0
Tentative: 	16
Rejected: 	463
Iteration: 	16 / 100
Confirmed: 	0
Tentative: 	14
Rejected: 	465
Iteration: 	17 / 100
Confirmed: 	0
Tentative: 	14
Rejected: 	465
Iteration: 	18 / 100
Confirmed: 	0
Tentative: 	14
Rejected: 	465
Iteration: 	19 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	20 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	21 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	22 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	23 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	24 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	25 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	26 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	27 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	28 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	29 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	30 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	31 / 100
Confirmed: 	3
Tentative: 	9
Rejected: 	467
Iteration: 	32 / 100
Confirmed: 	3
Tentative: 	8
Rejected: 	468
Iteration: 	33 / 100
Confirmed: 	3
Tentative: 	8
Rejected: 	468
Iteration: 	34 / 100
Confirmed: 	3
Tentative: 	8
Rejected: 	468
Iteration: 	35 / 100
Confirmed: 	3
Tentative: 	8
Rejected: 	468
Iteration: 	36 / 100
Confirmed: 	3
Tentative: 	8
Rejected: 	468
Iteration: 	37 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	38 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	39 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	40 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	41 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	42 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	43 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	44 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	45 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	46 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	47 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	48 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	49 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	50 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	51 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	52 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	53 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	54 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	55 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	56 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	57 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	58 / 100
Confirmed: 	5
Tentative: 	6
Rejected: 	468
Iteration: 	59 / 100
Confirmed: 	6
Tentative: 	5
Rejected: 	468
Iteration: 	60 / 100
Confirmed: 	6
Tentative: 	5
Rejected: 	468
Iteration: 	61 / 100
Confirmed: 	6
Tentative: 	5
Rejected: 	468
Iteration: 	62 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	63 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	64 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	65 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	66 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	67 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	68 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	69 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	70 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	71 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	72 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	73 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	74 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	75 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	76 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	77 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	78 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	79 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	80 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	81 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	82 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	83 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	84 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	85 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	86 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	87 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	88 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	89 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	90 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	91 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	92 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	93 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	94 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	95 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	96 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	97 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	98 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469
Iteration: 	99 / 100
Confirmed: 	6
Tentative: 	4
Rejected: 	469

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[32], line 22
     20 y = y.ravel()
     21 # Find all relevant features
---> 22 feat_selector.fit(X_numpy, y)
     24 # Check selected features
     25 selected_features = feat_selector.support_

File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\boruta\boruta_py.py:201, in BorutaPy.fit(self, X, y)
    188 def fit(self, X, y):
    189     """
    190     Fits the Boruta feature selection with the provided estimator.
    191 
   (...)
    198         The target values.
    199     """
--> 201     return self._fit(X, y)

File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\boruta\boruta_py.py:319, in BorutaPy._fit(self, X, y)
    317 # basic result variables
    318 self.n_features_ = confirmed.shape[0]
--> 319 self.support_ = np.zeros(n_feat, dtype=np.bool)
    320 self.support_[confirmed] = 1
    321 self.support_weak_ = np.zeros(n_feat, dtype=np.bool)

File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\__init__.py:338, in __getattr__(attr)
    333     warnings.warn(
    334         f"In the future `np.{attr}` will be defined as the "
    335         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    337 if attr in __former_attrs__:
--> 338     raise AttributeError(__former_attrs__[attr])
    340 if attr == 'testing':
    341     import numpy.testing as testing

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

	user_id	label	date	odds	score
0	W4g5Uq0Ztpw6pZpIQS5COAxyVirSKzh5E2Z+	1	20220101	0.049469	554.468368
1	XI84WKIYuZw6oZmG723GdSD2/BXDBbQbRHMZ	0	20220101	0.015022	726.414753
2	U4ozUKIQvZsyo5yKZkOqQr4cvXjr+zDkDg79	0	20220101	0.032161	616.588408
3	WYM6WaoRvZ8zp5sWJ37jQiArrQ7fI6ekgvYp	0	20220101	0.028479	634.130063
4	W405Uq4XuJs+ppoTLIQzU/AMAh0Pm2CZonGM	0	20220101	0.041598	579.466206

	user_id	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00008	...	K00532	K00533	K00534	K00535	K00536	K00537	K00538	K00539	K00540	label
count	18983.000000	18983.000000	15525.000000	15525.000000	15525.000000	15525.000000	15525.000000	15525.000000	5989.000000	5989.000000	...	11770.000000	10097.000000	11067.000000	11770.000000	10097.000000	11067.000000	11770.000000	5501.000000	11770.000000	18983.000000
mean	9491.000000	4.392878	10.269050	10.212947	10.240838	10.240837	0.027738	0.004546	124.357322	124.357322	...	3113.269925	105.428755	318.119164	534.524194	0.740517	1.653926	2.517077	1.936739	232.172283	0.052679
std	5480.064416	4.448300	4.743384	4.774049	4.745396	4.745789	0.355435	0.058756	140.191103	140.191103	...	8341.664790	541.256947	1316.867180	2479.869085	1.566985	3.405173	5.240442	0.855993	998.975890	0.223397
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000
25%	4745.500000	0.000000	8.000000	7.000000	8.000000	8.000000	0.000000	0.000000	37.000000	37.000000	...	227.365000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000
50%	9491.000000	3.000000	12.000000	12.000000	12.000000	12.000000	0.000000	0.000000	100.000000	100.000000	...	1161.955000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000
75%	14236.500000	6.000000	13.000000	13.000000	13.000000	13.000000	0.000000	0.000000	178.000000	178.000000	...	3111.315000	41.880000	120.000000	202.862500	1.000000	2.000000	3.000000	3.000000	117.587500	0.000000
max	18982.000000	23.000000	24.000000	22.000000	22.000000	22.000000	9.000000	1.580000	1463.000000	1463.000000	...	307248.870000	23582.060000	34980.590000	140824.220000	33.000000	70.000000	119.000000	3.000000	46941.410000	1.000000

	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00013	K00014	...	K00536	K00537	K00538	K00540	count_of_nan	count_of_nan_zero	count_of_zero	label	Sum	Sum_abs
bank	1.000000	0.000306	-0.013739	-0.006725	-0.006654	0.093561	0.098165	-0.349894	0.123056	0.097912	...	-0.045770	-0.019802	-0.010929	0.019038	0.136366	0.084881	-0.153446	-0.025069	16.491215	40.968669
K00001	0.000306	1.000000	0.988602	0.997065	0.996989	0.033200	0.001374	0.024237	0.236718	0.229680	...	0.020486	0.059671	0.075644	-0.015687	-0.344764	-0.348567	0.277069	-0.057694	20.514920	44.350152
K00002	-0.013739	0.988602	1.000000	0.997181	0.997095	-0.117459	-0.133339	0.022320	0.228092	0.226284	...	0.016680	0.056504	0.072670	-0.015983	-0.346588	-0.351055	0.278088	-0.058246	19.192749	42.889091
K00003	-0.006725	0.997065	0.997181	1.000000	0.999965	-0.042824	-0.067564	0.023302	0.233297	0.228900	...	0.018513	0.058147	0.074282	-0.015890	-0.346652	-0.350788	0.278370	-0.058126	19.895962	43.625725
K00004	-0.006654	0.996989	0.997095	0.999965	1.000000	-0.042691	-0.068103	0.023302	0.233638	0.229296	...	0.018394	0.058051	0.074189	-0.015911	-0.346620	-0.350722	0.278367	-0.058121	19.890949	43.617091

	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00013	K00014	...	K00536	K00537	K00538	K00540	count_of_nan	count_of_nan_zero	count_of_zero	label	Sum	Sum_abs
bank	1.000000	0.015741	0.008667	0.011287	0.011317	0.094475	0.094515	-0.357972	0.088785	0.046519	...	0.012287	0.051247	0.052780	0.104050	0.066009	0.033411	-0.168107	-0.019689	26.756921	68.048190
K00001	0.015741	1.000000	0.988760	0.993006	0.993045	-0.001263	-0.001529	0.147826	0.188408	0.173633	...	0.070153	0.105425	0.125985	0.110310	-0.244324	-0.258224	0.123906	-0.050728	28.188426	60.015640
K00002	0.008667	0.988760	1.000000	0.996139	0.996126	-0.090401	-0.090631	0.145985	0.183760	0.173100	...	0.068412	0.102437	0.122582	0.106690	-0.246793	-0.259606	0.126451	-0.050982	27.549275	59.397995
K00003	0.011287	0.993006	0.996139	1.000000	0.999916	-0.058499	-0.058769	0.146557	0.185520	0.173557	...	0.068922	0.103270	0.123522	0.107637	-0.246372	-0.259432	0.126144	-0.050829	27.754885	59.571441
K00004	0.011317	0.993045	0.996126	0.999916	1.000000	-0.058330	-0.058598	0.146557	0.185598	0.173634	...	0.068867	0.103221	0.123472	0.107592	-0.246367	-0.259413	0.126147	-0.050834	27.752297	59.566785

	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00013	K00014	...	K00536	K00537	K00538	K00540	count_of_nan	count_of_nan_zero	count_of_zero	label	Sum	Sum_abs
bank	1.000000	0.023824	0.013890	0.017503	0.017544	0.108101	0.108130	-0.466259	0.110389	0.055778	...	0.010902	0.051133	0.054243	0.104787	0.090837	0.053303	-0.228824	-0.022700	32.565850	83.406895
K00001	0.023824	1.000000	0.991139	0.995799	0.995794	-0.001527	-0.001789	0.201014	0.261068	0.241096	...	0.086221	0.134749	0.164304	0.141749	-0.347728	-0.357697	0.187914	-0.059422	35.150522	75.071861
K00002	0.013890	0.991139	1.000000	0.998883	0.998845	-0.106005	-0.106105	0.198237	0.254915	0.240370	...	0.084085	0.131006	0.159978	0.137037	-0.351544	-0.359849	0.191962	-0.059736	34.423049	74.402290
K00003	0.017503	0.995799	0.998883	1.000000	0.999986	-0.068729	-0.068884	0.199135	0.257284	0.241123	...	0.084692	0.132070	0.161188	0.138341	-0.350911	-0.359614	0.191304	-0.059570	34.666556	74.608073
K00004	0.017544	0.995794	0.998845	0.999986	1.000000	-0.068526	-0.068683	0.199135	0.257405	0.241241	...	0.084624	0.132008	0.161124	0.138277	-0.350892	-0.359584	0.191305	-0.059574	34.662795	74.601148

	l1_score	l2_score	l3_score	user_id	trace_id	query_date	bank	account	K00001	K00002	...	K00531	K00532	K00533	K00534	K00535	K00536	K00537	K00538	K00539	K00540
0	503.0	703.0	603	W447WaIXvpw5qZB1SwSBo8G68qQHO0TApmQa	388012889408315575-1680572652663	2022-10-30 00:00:00	Caixa	388012889408315575	-999999	-999999	...	-999999	-999999.00	-999999.0	-999999.0	-999999.00	-999999	-999999	-999999	-999999	-999999.00
1	547.0	682.0	614	W4kyVq4XuJI/oZ8x7Vb3pIxZ1T0iwgfNFzfd	5e35ff2760cfaf39b76e9036adfed6407d3d9f126f1fb6...	2022-12-02 00:00:00	Santander	5e35ff2760cfaf39b76e9036adfed6407d3d9f126f1fb6...	18	18	...	-999999	-999999.00	-999999.0	-999999.0	-999999.00	-999999	-999999	-999999	-999999	-999999.00
2	503.0	703.0	603	Woo7WKITvp8+qJyTjX0xqOCMl2x5Ac9ugLHU	402931997-1680572663409	2022-12-02 00:00:00	MercadoPago	402931997	2	2	...	-999999	-999999.00	-999999.0	-999999.0	-999999.00	-999999	-999999	-999999	-999999	-999999.00
3	570.0	712.0	641	W4oyVK8SvJ85qJ8hc8NBfY6nWrcoALZfQJTp	OF3ba40ee5145c91b167c78cb6a2f75a5a732878417369...	2022-11-28 00:00:00	Sicoob	OF3ba40ee5145c91b167c78cb6a2f75a5a732878417369...	11	11	...	3	2581.63	0.0	8701.8	10438.56	0	10	16	2	5219.28
4	571.0	668.0	619	W4oyVK8SvJ85qJ8hc8NBfY6nWrcoALZfQJTp	7bf543c57e1e324ab391a982233b3149b1c76e05071183...	2022-11-28 00:00:00	Santander	7bf543c57e1e324ab391a982233b3149b1c76e05071183...	11	11	...	3	2581.63	0.0	8701.8	10438.56	0	10	16	2	5219.28

	l1_score	l2_score	l3_score	user_id	trace_id	query_date	bank	account	K00001	K00002	...	K00531	K00532	K00533	K00534	K00535	K00536	K00537	K00538	K00539	K00540
604577	514.0	641.0	577	WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb	cb16cd854c4ae1d22c1f5f9c6643ac3d33eb4394ebfa4d...	2022-12-14 00:00:00	Santander	cb16cd854c4ae1d22c1f5f9c6643ac3d33eb4394ebfa4d...	12	5	...	3	652.22	0.0	0.0	285.67	0	0	2	1	285.67
604578	525.0	686.0	605	WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb	068d013b117e5a139ec850341f3e4559c84f2a4af29791...	2022-12-14 00:00:00	Next	068d013b117e5a139ec850341f3e4559c84f2a4af29791...	12	5	...	3	652.22	0.0	0.0	285.67	0	0	2	1	285.67
604579	518.0	576.0	547	WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb	6079a839-48c5-4d98-9a6a-9c6e34736209-3-1680620...	2022-12-14 00:00:00	Nubank	6079a839-48c5-4d98-9a6a-9c6e34736209	12	5	...	3	652.22	0.0	0.0	285.67	0	0	2	1	285.67
604580	499.0	692.0	595	X4s5WaIQtpgzpJ1TOrLe9AZYVGzIOGlyPWz5	fa915d1f82390ddb4db0da928bd5d18b3e5398a89f0b0b...	2022-12-09 00:00:00	Bradesco	fa915d1f82390ddb4db0da928bd5d18b3e5398a89f0b0b...	14	14	...	-999999	0.00	-999999.0	-999999.0	20.00	-999999	-999999	1	1	20.00
604581	503.0	699.0	601	X4s5WaIQtpgzpJ1TOrLe9AZYVGzIOGlyPWz5	36713d9dde89ce8e7ae48186ef03b2602512d771f01855...	2022-12-09 00:00:00	Bradescard	36713d9dde89ce8e7ae48186ef03b2602512d771f01855...	14	14	...	-999999	0.00	-999999.0	-999999.0	20.00	-999999	-999999	1	1	20.00

	l1_score	l2_score	l3_score	user_id	trace_id	query_date	bank	account	K00001	K00002	...	K00535	K00536	K00538	K00539	K00540	date	odds	score
0	481.0	613.0	547	W4s4U64Tv5k6qZFvl9hopJiD/eMwRzhfzgzj	274617943-1680572656066	2022-12-01 00:00:00	MercadoPago	274617943	14	14	...	0.0	-999999	0	-999999	0.0	20220618	0.011838	760.777437
1	596.0	689.0	642	X4k/UqsXuJIzp597GhwRlfGYV/Tl2LEor+yR	f6f3aa07-c979-3c4e-803e-800671bfdc9f-168057266...	2022-12-12 00:00:00	Iti	f6f3aa07-c979-3c4e-803e-800671bfdc9f	19	19	...	0.0	0	0	-999999	0.0	20220419	0.020568	681.077800
2	516.0	717.0	616	XIo4VqMZtpg6pZ8jUBLtIAT3+QxAvtJgglyi	480800120-1680572688271	2022-12-03 00:00:00	MercadoPago	480800120	13	13	...	20.0	0	1	1	20.0	20220328	0.011282	767.713355
3	598.0	703.0	650	W4IyU64TvZkzppDR7Y0/88NNaw+ppq2KBHRe	36859416b1a2c8641c76c31354286369ab32ddbeabfb1e...	2022-12-03 00:00:00	Bradesco	36859416b1a2c8641c76c31354286369ab32ddbeabfb1e...	21	21	...	0.0	0	0	-999999	0.0	20220122	0.077031	490.576321
4	636.0	703.0	669	WYI8UKoXup8zoZtU7kDu0DFpIQqtDCDXw4Ob	99c3f85f-be56-3d5b-82f0-d5e476585cd2-168057267...	2022-11-25 00:00:00	Itaú	99c3f85f-be56-3d5b-82f0-d5e476585cd2	1	1	...	0.0	0	0	-999999	0.0	20220412	0.012872	748.694241

	user_id	bank	K00001	K00002	K00003	K00004	K00007	K00008	...	K00532	K00533	K00535	K00536	K00538	K00539	K00540
0	W4s4U64Tv5k6qZFvl9hopJiD/eMwRzhfzgzj	MercadoPago	14	14	14.0	14.0	-999999	-999999	...	3000.00	-999999.0	0.0	-999999	0	-999999	0.0
1	X4k/UqsXuJIzp597GhwRlfGYV/Tl2LEor+yR	Iti	19	19	19.0	19.0	41	41	...	2964.20	0.0	0.0	0	0	-999999	0.0
2	XIo4VqMZtpg6pZ8jUBLtIAT3+QxAvtJgglyi	MercadoPago	13	13	13.0	13.0	-999999	-999999	...	1280.73	0.0	20.0	0	1	1	20.0
3	W4IyU64TvZkzppDR7Y0/88NNaw+ppq2KBHRe	Bradesco	21	21	21.0	21.0	310	310	...	0.00	0.0	0.0	0	0	-999999	0.0
4	WYI8UKoXup8zoZtU7kDu0DFpIQqtDCDXw4Ob	Itaú	1	1	1.0	1.0	257	257	...	0.00	0.0	0.0	0	0	-999999	0.0

	user_id	bank	K00001	K00002	K00003	K00004	K00007	K00008	...	K00532	K00533	K00534	K00535	K00536	K00537	K00538	K00539	K00540
26819	18978	2	13.0	13.0	13.0	13.0	95.0	95.0	...	0.00	0.0	0.0	0.0	0.0	0.0	0.0	NaN	0.0
26820	18979	0	8.0	8.0	8.0	8.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
26821	18980	2	16.0	16.0	16.0	16.0	217.0	217.0	...	3199.50	20.0	1090.0	1155.0	1.0	6.0	10.0	3.0	385.0
26822	18981	0	0.0	0.0	0.0	0.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
26823	18982	0	12.0	12.0	12.0	12.0	NaN	NaN	...	3084.14	12.0	32.0	42.0	1.0	2.0	3.0	3.0	14.0

	K00001_RobustZScore	K00001_IsolationForest	K00001_OneClassSVM	K00001_IQR	K00001_ZScore	K00002_RobustZScore	K00002_IsolationForest	K00002_OneClassSVM	K00002_IQR	K00002_ZScore	...	K00539_RobustZScore	K00539_IsolationForest	K00539_OneClassSVM	K00539_IQR	K00539_ZScore	K00540_RobustZScore	K00540_IsolationForest	K00540_OneClassSVM	K00540_IQR	K00540_ZScore
0	1.000000	0.0	0.0	0	0	1.000000	0.0	0.0	0	0	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
1	2.666667	1.0	1.0	0	1	2.666667	1.0	1.0	0	1	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
2	0.666667	0.0	0.0	0	0	0.666667	0.0	0.0	0	0	...	0.0	0.0	0.0	0	0	0.0	0.0	0.0	0	0
3	3.333333	1.0	1.0	0	1	3.333333	1.0	1.0	0	1	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
4	3.333333	0.0	0.0	0	1	3.333333	0.0	0.0	0	1	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18978	0.666667	0.0	0.0	0	0	0.666667	0.0	0.0	0	0	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
18979	1.000000	0.0	1.0	0	0	1.000000	0.0	0.0	0	0	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
18980	1.666667	0.0	0.0	0	1	1.666667	0.0	0.0	0	1	...	0.0	1.0	1.0	1	1	0.0	1.0	1.0	1	0
18981	3.666667	0.0	0.0	0	1	3.666667	0.0	1.0	0	1	...	0.0	0.0	1.0	0	0	0.0	0.0	1.0	0	0
18982	0.333333	0.0	1.0	0	0	0.333333	0.0	0.0	0	0	...	0.0	1.0	1.0	1	1	0.0	0.0	0.0	0	0

	count	mean	std	5%	10%	15%	20%	25%	...	65%	70%	75%	80%	85%	90%	95%	100%	max	mode
user_id	18983.0	9491.0	5480.0	949.0	1898.0	2847.0	3796.0	4746.0	...	12338.0	13287.0	14236.0	15186.0	16135.0	17084.0	18033.0	18982.0	18982.0	0.0
bank	18983.0	4.0	4.0	0.0	0.0	0.0	0.0	0.0	...	5.0	5.0	6.0	9.0	10.0	11.0	11.0	23.0	23.0	0.0
K00001	15525.0	10.0	5.0	1.0	2.0	4.0	6.0	8.0	...	12.0	13.0	13.0	13.0	14.0	15.0	17.0	24.0	24.0	12.0
K00002	15525.0	10.0	5.0	1.0	2.0	4.0	6.0	7.0	...	12.0	13.0	13.0	13.0	14.0	15.0	17.0	22.0	22.0	12.0
K00003	15525.0	10.0	5.0	1.0	2.0	4.0	6.0	8.0	...	12.0	13.0	13.0	13.0	14.0	15.0	17.0	22.0	22.0	12.0

label	0	1	total_counts	percentage_1_over_0
bank
0	4660	292	4952	5.9
3	3106	173	3279	5.3
4	1951	88	2039	4.3
11	1800	61	1861	3.3
5	1492	102	1594	6.4
2	1031	47	1078	4.4
9	1005	56	1061	5.3
6	850	59	909	6.5
1	749	57	806	7.1
8	210	13	223	5.8
10	171	10	181	5.5
13	155	13	168	7.7

	Sum_pearson	Sum_abs_pearson	Sum_kendall	Sum_abs_kendall	Sum_spearman	Sum_abs_spearman
K00540	38.2	81.1	70.2	145.0	84.2	174.6
count_of_nan	-32.2	71.5	-51.1	108.4	-69.6	146.4
count_of_nan_zero	-64.5	134.2	-84.3	174.2	-111.9	230.3
count_of_zero	-26.8	65.7	-46.2	103.2	-58.9	132.7
label	-2.8	12.3	-6.9	19.1	-8.2	21.9

	Feature	IV
102	K00117	0.29
267	K00307	0.29
101	K00116	0.27
476	count_of_nan	0.26
141	K00156	0.26
244	K00282	0.26
103	K00118	0.26
140	K00155	0.25
142	K00157	0.24
90	K00105	0.24
78	K00093	0.24
273	K00313	0.24
243	K00281	0.24
165	K00180	0.23
245	K00283	0.23
268	K00308	0.23
89	K00104	0.23
79	K00094	0.23
77	K00092	0.23
107	K00122	0.23
108	K00123	0.23
85	K00100	0.23
91	K00106	0.22
477	count_of_nan_zero	0.22
166	K00181	0.22

	Imputer Strategy	Solver	Pipeline	Accuracy	Precision	Recall	F1	KS Statistic	Gini Coefficient	AUC
0	mean	newton-cg	mean	0.931335	0.600000	0.002778	0.005128	0.269583	0.071454	0.535727
1	mean	newton-cg	norm	0.931160	0.600000	0.000000	0.000000	0.276055	0.113549	0.556775
2	mean	newton-cg	std	0.910081	0.026944	0.013675	0.017743	0.251698	0.120369	0.560184
3	mean	newton-cg	quan	0.907793	0.092103	0.048291	0.061166	0.261790	0.079012	0.539506
4	mean	newton-cg	kbins	0.921145	0.083333	0.002778	0.004762	0.259589	0.088270	0.544135
5	mean	newton-cg	pca	0.601877	0.347858	0.429274	0.090246	0.262850	0.113502	0.556751
6	mean	newton-cg	svd	0.933791	0.700000	0.016239	0.030037	0.250993	0.149492	0.574746
7	mean	newton-cg	power	0.916578	0.097910	0.032051	0.046799	0.259335	0.130656	0.565328
8	mean	newton-cg	MinMaxpower	0.934846	0.933333	0.008120	0.015018	0.249254	0.116394	0.558197
9	mean	lbfgs	mean	0.931335	0.600000	0.002778	0.005128	0.269583	0.071454	0.535727
10	mean	lbfgs	norm	0.931160	0.600000	0.000000	0.000000	0.276055	0.113549	0.556775
11	mean	lbfgs	std	0.910081	0.026944	0.013675	0.017743	0.251698	0.120369	0.560184
12	mean	lbfgs	quan	0.907793	0.092103	0.048291	0.061166	0.261790	0.079012	0.539506
13	mean	lbfgs	kbins	0.921145	0.083333	0.002778	0.004762	0.259589	0.088270	0.544135
14	mean	lbfgs	pca	0.601877	0.347858	0.429274	0.090246	0.262850	0.113471	0.556735

	Resampling Technique	Class Weight	Accuracy	F1 Score	Precision	ROC AUC	Balanced F1 Score	Balanced Accuracy	Kappa	Gini Coefficient	MCC	KS Statistic	AUC PRC	Sample shape X class0	Sample shape X class1
0	Original	With Class Weight	0.686842	0.131387	0.080357	0.534930	0.764410	0.534930	0.026690	0.069859	0.037987	0.199437	0.082904	(1419, 480)	(99, 480)
1	ADASYN	With Class Weight	0.713158	0.141732	0.088235	0.549014	0.782668	0.549014	0.040315	0.098028	0.054842	0.142535	0.070891	(1419, 480)	(1461, 480)
2	SMOTETomek	With Class Weight	0.705263	0.125000	0.077670	0.526197	0.776878	0.526197	0.021384	0.052394	0.029222	0.145352	0.071110	(1379, 480)	(1379, 480)
3	SMOTEENN	With Class Weight	0.671053	0.125874	0.076271	0.526479	0.753227	0.526479	0.019405	0.052958	0.028374	0.162817	0.075604	(829, 480)	(1221, 480)
4	SMOTE	With Class Weight	0.707895	0.125984	0.078431	0.527606	0.778680	0.527606	0.022706	0.055211	0.030888	0.148169	0.074353	(1419, 480)	(709, 480)
5	EditedNearestNeighbours	With Class Weight	0.697368	0.135338	0.083333	0.540563	0.771768	0.540563	0.031901	0.081127	0.044591	0.139718	0.069284	(1168, 480)	(99, 480)
6	TomekLinks	With Class Weight	0.694737	0.134328	0.082569	0.539155	0.769935	0.539155	0.030570	0.078310	0.042924	0.182535	0.076210	(1384, 480)	(99, 480)
7	ClusterCentroids	With Class Weight	0.442105	0.123967	0.069124	0.515493	0.560025	0.515493	0.006781	0.030986	0.015521	0.120563	0.066399	(99, 480)	(99, 480)
8	NearMiss	With Class Weight	0.239474	0.132132	0.071429	0.537183	0.310616	0.537183	0.011877	0.074366	0.047046	0.154930	0.071762	(99, 480)	(99, 480)
9	RepeatedEditedNearestNeighbours	With Class Weight	0.681579	0.116788	0.071429	0.513521	0.760450	0.513521	0.010331	0.027042	0.014705	0.116056	0.065356	(1030, 480)	(99, 480)

	Resampling Technique	Class Weight	Accuracy	F1 Score	Precision	ROC AUC	Balanced F1 Score	Balanced Accuracy	Kappa	Gini Coefficient	MCC	KS Statistic	AUC PRC	Sample shape X class0	Sample shape X class1
0	Original	None	0.95	0.01	0.20	0.50	0.92	0.50	0.01	0.00	0.02	0.19	0.08	14386	800
1	Original	balanced	0.58	0.13	0.07	0.59	0.70	0.59	0.04	0.19	0.08	0.22	0.08	14386	800
2	ADASYN	None	0.59	0.14	0.08	0.61	0.70	0.61	0.05	0.23	0.10	0.24	0.08	14386	14184
3	ADASYN	balanced	0.59	0.14	0.08	0.60	0.70	0.60	0.05	0.20	0.09	0.22	0.08	14386	14184
4	SMOTETomek	None	0.60	0.14	0.08	0.61	0.71	0.61	0.05	0.21	0.10	0.24	0.08	14129	14129
5	SMOTETomek	balanced	0.60	0.14	0.08	0.60	0.71	0.60	0.05	0.20	0.09	0.22	0.08	14160	14160
6	SMOTEENN	None	0.48	0.13	0.07	0.59	0.61	0.59	0.03	0.19	0.08	0.22	0.08	9201	12942
7	SMOTEENN	balanced	0.58	0.14	0.08	0.60	0.69	0.60	0.05	0.21	0.09	0.24	0.08	9196	12995
8	SMOTE	None	0.64	0.14	0.08	0.60	0.74	0.60	0.05	0.21	0.10	0.23	0.08	14386	12947
9	SMOTE	balanced	0.59	0.14	0.08	0.60	0.70	0.60	0.05	0.21	0.09	0.23	0.08	14386	12947
10	EditedNearestNeighbours	None	0.95	0.01	0.10	0.50	0.92	0.50	0.00	0.00	0.01	0.20	0.08	12328	800
11	EditedNearestNeighbours	balanced	0.58	0.14	0.08	0.60	0.69	0.60	0.05	0.21	0.09	0.21	0.08	12328	800
12	TomekLinks	None	0.95	0.01	0.50	0.50	0.92	0.50	0.01	0.00	0.05	0.18	0.08	14081	800
13	TomekLinks	balanced	0.58	0.13	0.07	0.59	0.70	0.59	0.04	0.18	0.08	0.22	0.08	14081	800
14	ClusterCentroids	None	0.27	0.11	0.06	0.53	0.36	0.53	0.01	0.06	0.03	0.19	0.07	800	800
15	ClusterCentroids	balanced	0.27	0.11	0.06	0.54	0.36	0.54	0.01	0.09	0.05	0.20	0.07	800	800
16	NearMiss	None	0.14	0.08	0.04	0.42	0.18	0.42	-0.02	-0.16	-0.11	0.22	0.04	800	800
17	NearMiss	balanced	0.14	0.08	0.04	0.42	0.18	0.42	-0.02	-0.16	-0.11	0.22	0.04	800	800
18	RepeatedEditedNearestNeighbours	None	0.94	0.02	0.10	0.50	0.92	0.50	0.01	0.00	0.01	0.22	0.08	11309	800
19	RepeatedEditedNearestNeighbours	balanced	0.57	0.14	0.08	0.61	0.69	0.61	0.05	0.21	0.09	0.22	0.08	11309	800
20	RandomUnderSampler	None	0.51	0.13	0.07	0.59	0.63	0.59	0.04	0.18	0.08	0.21	0.07	800	800
21	RandomUnderSampler	balanced	0.52	0.12	0.07	0.58	0.64	0.58	0.03	0.15	0.07	0.18	0.07	800	800

	Feature	IV
284	K00325	0.0
285	K00326	0.0
286	K00327	0.0
287	K00328	0.0
288	K00329	0.0
289	K00338	0.0
290	K00339	0.0
291	K00340	0.0
292	K00341	0.0
293	K00342	0.0
294	K00343	0.0
296	K00354	0.0
322	K00382	0.0
299	K00357	0.0
310	K00370	0.0
311	K00371	0.0
312	K00372	0.0
313	K00373	0.0
314	K00374	0.0
315	K00375	0.0
316	K00376	0.0
317	K00377	0.0
319	K00379	0.0
320	K00380	0.0
479	label	0.0

	user_id	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00013	...	K00534	K00535	K00536	K00537	K00538	K00540	count_of_nan	count_of_nan_zero	count_of_zero
15695	15695	3	17.0	17.0	17.0	17.0	0.0	0.0	249.0	5.0	...	0.0	0.0	0.0	0.0	0.0	0.0	23	328	305
16205	16205	3	NaN	NaN	NaN	NaN	NaN	NaN	194.0	16.0	...	NaN	NaN	NaN	NaN	NaN	NaN	312	443	131
9060	9060	5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	519	539	20
11597	11597	5	14.0	14.0	14.0	14.0	0.0	0.0	60.0	8.0	...	NaN	NaN	NaN	NaN	NaN	NaN	258	450	192
8561	8561	3	8.0	8.0	8.0	8.0	0.0	0.0	1463.0	NaN	...	0.0	0.0	NaN	0.0	0.0	0.0	160	447	287

	user_id	bank	K00001	K00002	K00003	K00004	K00005	K00006	K00007	K00013	...	K00534	K00535	K00536	K00537	K00538	K00540	count_of_nan	count_of_nan_zero	count_of_zero	label
0	0	0	0.583333	0.636364	0.636364	0.636364	0.0	0.0	NaN	0.000000	...	0.000000	0.000000	NaN	0.000000	0.000000	0.000000	0.225743	0.733945	0.842541	0
1	1	1	0.791667	0.863636	0.863636	0.863636	0.0	0.0	0.028025	0.358974	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.019802	0.495413	0.914365	0
2	2	0	0.541667	0.590909	0.590909	0.590909	0.0	0.0	NaN	0.128205	...	0.000000	0.000142	0.000000	0.000000	0.008403	0.000426	0.150495	0.651376	0.872928	0
3	3	2	0.875000	0.954545	0.954545	0.954545	0.0	0.0	0.211893	NaN	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.326733	0.792049	0.754144	0
4	4	3	0.041667	0.045455	0.045455	0.045455	0.0	0.0	0.175666	NaN	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.158416	0.516820	0.740331	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18978	18978	2	0.541667	0.590909	0.590909	0.590909	0.0	0.0	0.064935	0.358974	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.180198	0.474006	0.671271	0
18979	18979	0	0.333333	0.363636	0.363636	0.363636	0.0	0.0	NaN	0.128205	...	NaN	NaN	NaN	NaN	NaN	NaN	0.960396	0.957187	0.019337	0
18980	18980	2	0.666667	0.727273	0.727273	0.727273	0.0	0.0	0.148325	0.179487	...	0.031160	0.008202	0.030303	0.085714	0.084034	0.008202	0.011881	0.238532	0.693370	0
18981	18981	0	0.000000	0.000000	0.000000	0.000000	0.0	0.0	NaN	0.051282	...	NaN	NaN	NaN	NaN	NaN	NaN	0.960396	0.969419	0.030387	0
18982	18982	0	0.500000	0.545455	0.545455	0.545455	0.0	0.0	NaN	0.282051	...	0.000915	0.000298	0.030303	0.028571	0.025210	0.000298	0.029703	0.422018	0.834254	0

	user_id	bank	K00001	K00005	K00006	K00007	K00013	K00014	K00017	K00018	...	K00529	K00530	K00534	K00535	K00537	K00538	count_of_nan	count_of_nan_zero	count_of_zero	label
0	0	0	14.0	0.0	0.0	NaN	0.0	0.0	0.0	0.0	...	1.0	1.0	0.0	0.0	0.0	0.0	129	453	324	0
1	1	1	19.0	0.0	0.0	41.0	14.0	14.0	0.0	0.0	...	50.0	123.0	0.0	0.0	0.0	0.0	25	375	350	0
2	2	0	13.0	0.0	0.0	NaN	5.0	5.0	0.0	0.0	...	13.0	23.0	0.0	20.0	0.0	1.0	91	426	335	0
3	3	2	21.0	0.0	0.0	310.0	NaN	NaN	NaN	NaN	...	0.0	0.0	0.0	0.0	0.0	0.0	180	472	292	0
4	4	3	1.0	0.0	0.0	257.0	NaN	NaN	NaN	NaN	...	0.0	0.0	0.0	0.0	0.0	0.0	95	382	287	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18978	18978	2	13.0	0.0	0.0	95.0	14.0	14.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	106	368	262	0
18979	18979	0	8.0	0.0	0.0	NaN	5.0	5.0	0.0	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	500	526	26	0
18980	18980	2	16.0	0.0	0.0	217.0	7.0	7.0	0.0	0.0	...	17.0	27.0	1090.0	1155.0	6.0	10.0	21	291	270	0
18981	18981	0	0.0	0.0	0.0	NaN	2.0	2.0	0.0	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	500	530	30	0
18982	18982	0	12.0	0.0	0.0	NaN	11.0	11.0	0.0	0.0	...	73.0	114.0	32.0	42.0	2.0	3.0	30	351	321	0

	Feature	F-Score
287	count_of_nan	164.506769
288	count_of_nan_zero	158.560306
183	K00312	153.797590
289	count_of_zero	135.795018
169	K00287	94.272894
276	K00491	92.833814
170	K00288	89.596179
277	K00492	85.854319
171	K00289	74.787517
71	K00102	53.172934
72	K00103	51.252739
70	K00101	45.230959
274	K00488	43.870335
275	K00489	43.828140
98	K00160	43.019259
97	K00159	42.885576
103	K00165	41.921435
104	K00166	41.718600
118	K00183	41.606834
182	K00311	41.459174
181	K00310	39.901792
164	K00282	39.381416
102	K00164	38.776881
96	K00158	38.758512
282	K00530	38.209025
119	K00184	37.940539
281	K00529	37.858352
117	K00182	36.152356
165	K00283	35.804047
9	K00027	34.840540
180	K00309	33.989225
4	K00013	33.874638
66	K00097	32.893723
65	K00096	32.872810
0	K00001	32.361230
5	K00014	32.184726
163	K00281	31.767651
178	K00307	30.813072
64	K00095	30.375648
95	K00157	29.961412

	K00001	K00007	K00013	K00014	K00017	K00027	K00028	K00035	K00041	K00047	...	K00527	K00529	K00530	K00534	K00535	K00537	K00538	count_of_nan	count_of_nan_zero	count_of_zero
0	14.0	NaN	0.0	0.0	0.0	2	0.0	NaN	NaN	NaN	...	3000.000000	1.0	1.0	0.0	0.0	0.0	0.0	129	453	324
1	19.0	41.0	14.0	14.0	0.0	1	7000.0	0.0	0.00	0.24	...	8892.599609	50.0	123.0	0.0	0.0	0.0	0.0	25	375	350
2	13.0	NaN	5.0	5.0	0.0	2	0.0	NaN	NaN	NaN	...	3842.199951	13.0	23.0	0.0	20.0	0.0	1.0	91	426	335
3	21.0	310.0	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	...	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	180	472	292
4	1.0	257.0	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	...	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	95	382	287
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18978	13.0	95.0	14.0	14.0	0.0	1	7000.0	0.0	0.09	0.18	...	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	106	368	262
18979	8.0	NaN	5.0	5.0	0.0	2	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	500	526	26
18980	16.0	217.0	7.0	7.0	0.0	1	NaN	0.0	0.40	0.45	...	9598.500000	17.0	27.0	1090.0	1155.0	6.0	10.0	21	291	270
18981	0.0	NaN	2.0	2.0	0.0	2	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	500	530	30
18982	12.0	NaN	11.0	11.0	0.0	1	0.0	0.0	0.01	0.47	...	9252.429688	73.0	114.0	32.0	42.0	2.0	3.0	30	351	321

	K00092	K00093	K00094	K00099	K00100	K00155	K00156	K00157	K00158	K00159	...	K00312	K00395	K00485	K00486	K00526	K00527	K00530	count_of_nan	count_of_nan_zero	count_of_zero
0	74.199997	3074.199951	3074.199951	3083.070068	3083.070068	0.000000	3000.000000	3000.000000	0.0	1.0	...	1.0	0.000000	3000.000000	3000.000000	3000.000000	3000.000000	1.0	129	453	324
1	3825.590088	10242.940430	20437.449219	10324.440430	20714.269531	3825.340088	10201.349609	20365.519531	20.0	103.0	...	3.0	0.000000	4040.330078	8892.599609	4040.330078	8892.599609	123.0	25	375	350
2	1271.839966	2003.540039	4292.620117	2780.510010	5755.069824	1271.839966	2003.540039	4292.620117	11.0	18.0	...	3.0	0.000000	1777.000000	3862.199951	1777.000000	3842.199951	23.0	91	426	335
3	0.000000	0.000000	0.000000	21770.460938	21770.460938	0.000000	0.000000	0.000000	0.0	0.0	...	NaN	0.000000	20959.980469	20959.980469	0.000000	0.000000	0.0	180	472	292
4	8721.959961	10261.959961	10261.959961	8723.849609	8723.849609	0.010000	40.009998	40.009998	1.0	2.0	...	NaN	0.000000	378.500000	378.500000	0.000000	0.000000	0.0	95	382	287
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
26819	1565.000000	1565.000000	1565.000000	4876.439941	4876.439941	1565.000000	1565.000000	1565.000000	2.0	2.0	...	NaN	115.059998	1204.420044	1204.420044	0.000000	0.000000	0.0	106	368	262
26820	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	500	526	26
26821	16270.559570	76942.039062	100723.296875	76431.796875	103675.773438	4197.600098	26182.599609	33165.699219	13.0	26.0	...	3.0	90.000000	8654.040039	13834.540039	4483.000000	9598.500000	27.0	21	291	270
26822	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	500	530	30
26823	6671.049805	10642.000000	16146.040039	10849.290039	16147.509766	5113.000000	8078.399902	10763.500000	22.0	57.0	...	3.0	32.000000	6500.120117	9294.429688	6468.120117	9252.429688	114.0	30	351	321

	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
0	Combined	2071	1526	69	131	0.940509	0.95674	0.615379	0.376115	0.230758	0.230758	0.103938	0.051973	0.579932	0.615379	0.141088	0.691383	0.079059	0.655
1	Class 0	2071	131	1526	69	0.940509	0.95674	0.615379	0.376115	0.230758	0.230758	0.103938	0.051973	0.579932	0.615379	0.141088	0.691383	0.079059	0.655
2	Class 1	131	2071	69	1526	0.940509	0.95674	0.615379	0.376115	0.230758	0.230758	0.103938	0.051973	0.579932	0.615379	0.141088	0.691383	0.079059	0.655

	Algorithm	Hyperparameters	Imputation Technique	Imbalance Class Technique	Model Unique Code	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
0	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	None	k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_0	Class 1	3550	10	237	0	1.000000	0.040486	0.561344	0.089385	0.122688	0.117866	-0.013259	-0.005080	0.934949	0.498596	0.000000	0.906062	0.000000	0.000000
1	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	None	k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_1	Class 1	3620	13	163	1	0.999724	0.073864	0.552757	0.066008	0.105515	0.098012	0.008450	0.004472	0.953648	0.501260	0.011236	0.934586	0.071429	0.006098
2	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	None	k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_2	Class 1	3600	10	187	0	1.000000	0.050761	0.540575	0.062454	0.081150	0.077690	-0.011696	-0.005025	0.948117	0.498615	0.000000	0.925430	0.000000	0.000000
3	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	None	k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_3	Class 1	3589	11	194	2	0.999443	0.053659	0.539393	0.069418	0.078787	0.073492	0.027077	0.012798	0.945996	0.503574	0.019139	0.923022	0.153846	0.010204
4	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	None	k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_4	Class 1	3575	5	214	2	0.999441	0.022831	0.574602	0.102078	0.149205	0.142753	0.042455	0.014416	0.942308	0.503931	0.017937	0.916091	0.285714	0.009259
5	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	None	k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_0	Class 1	3549	11	236	1	0.999718	0.044534	0.550183	0.086808	0.100366	0.097887	0.004868	0.002028	0.934949	0.500565	0.008032	0.906554	0.083333	0.004219
6	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	None	k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_1	Class 1	3624	9	163	1	0.999724	0.052326	0.563737	0.070277	0.127473	0.115713	0.014360	0.006562	0.954701	0.501810	0.011494	0.935125	0.100000	0.006098
7	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	None	k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_2	Class 1	3596	14	186	1	0.999722	0.070000	0.561800	0.070924	0.123599	0.118447	0.005069	0.002606	0.947327	0.500735	0.009901	0.925514	0.066667	0.005348
8	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	None	k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_3	Class 1	3580	20	192	4	0.998884	0.094340	0.546001	0.081556	0.092003	0.086752	0.041466	0.025384	0.944152	0.507426	0.036364	0.922972	0.166667	0.020408
9	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	None	k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_4	Class 1	3566	14	213	3	0.999159	0.061674	0.584009	0.094202	0.168018	0.162270	0.034619	0.017594	0.940200	0.504989	0.025751	0.915472	0.176471	0.013889
10	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	None	k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_0	Class 1	3551	9	237	0	1.000000	0.036585	0.560442	0.090550	0.120884	0.114489	-0.012577	-0.004588	0.935212	0.498736	0.000000	0.906193	0.000000	0.000000
11	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	None	k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_1	Class 1	3613	20	163	1	0.999723	0.109290	0.562621	0.063424	0.125243	0.118232	0.001624	0.001015	0.951804	0.500296	0.010811	0.933642	0.047619	0.006098
12	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	None	k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_2	Class 1	3594	16	186	1	0.999722	0.079208	0.544127	0.067827	0.088253	0.080460	0.002967	0.001609	0.946800	0.500458	0.009804	0.925245	0.058824	0.005348
13	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	None	k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_3	Class 1	3582	18	195	1	0.999721	0.084507	0.549494	0.071411	0.098988	0.090743	0.000320	0.000178	0.943888	0.500051	0.009302	0.921464	0.052632	0.005102
14	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	None	k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_4	Class 1	3561	19	215	1	0.999719	0.081197	0.582975	0.091624	0.165950	0.158876	-0.002168	-0.001181	0.938356	0.499661	0.008475	0.913580	0.050000	0.004630
15	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_0	Class 1	3149	411	193	44	0.986220	0.680464	0.559812	0.120083	0.119624	0.119271	0.052294	0.049118	0.840927	0.535102	0.127168	0.863471	0.096703	0.185654
16	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_1	Class 1	3166	467	134	30	0.990613	0.777038	0.545952	0.088694	0.091903	0.093566	0.032778	0.027614	0.841717	0.527191	0.090772	0.877786	0.060362	0.182927
17	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_2	Class 1	3162	448	164	23	0.992779	0.732026	0.530319	0.076261	0.060638	0.071788	-0.000725	-0.000642	0.838820	0.499447	0.069909	0.870304	0.048832	0.122995
18	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_3	Class 1	3165	435	167	29	0.990920	0.722591	0.533114	0.092357	0.066229	0.068946	0.018325	0.016475	0.841412	0.513563	0.087879	0.870545	0.062500	0.147959
19	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_4	Class 1	3146	434	173	43	0.986516	0.714992	0.565178	0.115438	0.130355	0.131378	0.054405	0.049655	0.840095	0.538923	0.124098	0.867182	0.090147	0.199074
20	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_0	Class 1	3156	404	207	30	0.990584	0.661211	0.545113	0.100545	0.090225	0.099853	0.009959	0.009438	0.839083	0.506550	0.089419	0.860416	0.069124	0.126582
21	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_1	Class 1	3190	443	132	32	0.990068	0.770435	0.552373	0.081366	0.104746	0.104619	0.044970	0.038409	0.848565	0.536592	0.100156	0.882030	0.067368	0.195122
22	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_2	Class 1	3145	465	156	31	0.990239	0.748792	0.558970	0.091710	0.117940	0.127480	0.023737	0.020728	0.836450	0.518483	0.090776	0.869790	0.062500	0.165775
23	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_3	Class 1	3167	433	167	29	0.990926	0.721667	0.539427	0.078303	0.078853	0.085261	0.018735	0.016862	0.841939	0.513841	0.088146	0.870856	0.062771	0.147959
24	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_4	Class 1	3148	432	174	42	0.986834	0.712871	0.576148	0.110522	0.152297	0.158199	0.051699	0.047255	0.840358	0.536887	0.121739	0.867221	0.088608	0.194444
25	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_0	Class 1	3156	404	198	39	0.987793	0.671096	0.554961	0.111374	0.109923	0.111955	0.038487	0.036334	0.841454	0.525537	0.114706	0.863107	0.088036	0.164557
26	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_1	Class 1	3166	467	131	33	0.989684	0.780936	0.551633	0.080609	0.103266	0.107138	0.043692	0.036739	0.842507	0.536338	0.099398	0.878537	0.066000	0.201220
27	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_2	Class 1	3159	451	159	28	0.991214	0.739344	0.541171	0.087225	0.082343	0.090324	0.016164	0.014251	0.839347	0.512401	0.084084	0.871180	0.058455	0.149733
28	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_3	Class 1	3172	428	172	24	0.992491	0.713333	0.539729	0.090781	0.079459	0.088141	0.002432	0.002201	0.841939	0.501780	0.074074	0.870247	0.053097	0.122449
29	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Oversampling	k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_4	Class 1	3158	422	170	46	0.985643	0.712838	0.576763	0.116750	0.153527	0.155364	0.066999	0.061420	0.844046	0.547543	0.134503	0.869930	0.098291	0.212963
30	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_0	Class 1	2172	1388	114	123	0.946405	0.924101	0.598085	0.114938	0.196169	0.135390	0.063805	0.036793	0.604425	0.564550	0.140732	0.705476	0.081403	0.518987
31	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_1	Class 1	2251	1382	71	93	0.960324	0.951136	0.631722	0.113075	0.263444	0.186671	0.077858	0.038756	0.617329	0.593336	0.113484	0.728252	0.063051	0.567073
32	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_2	Class 1	2175	1435	80	107	0.953111	0.947195	0.608780	0.141824	0.217560	0.174686	0.076969	0.039385	0.601001	0.587343	0.123771	0.711256	0.069390	0.572193
33	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_3	Class 1	2288	1312	96	100	0.958124	0.931818	0.588710	0.118650	0.177421	0.145760	0.066734	0.037059	0.629083	0.572880	0.124378	0.731644	0.070822	0.510204
34	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Zero Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_4	Class 1	2160	1420	100	116	0.949033	0.934211	0.602570	0.111931	0.205139	0.168731	0.066260	0.036265	0.599579	0.570194	0.132420	0.705169	0.075521	0.537037
35	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_0	Class 1	2181	1379	108	129	0.944156	0.927371	0.602571	0.124466	0.205143	0.160987	0.077593	0.044804	0.608375	0.578472	0.147851	0.708448	0.085544	0.544304
36	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_1	Class 1	2258	1375	81	83	0.964545	0.944368	0.609237	0.118706	0.218475	0.162051	0.053344	0.026772	0.616539	0.563811	0.102343	0.727954	0.056927	0.506098
37	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_2	Class 1	2179	1431	76	111	0.951528	0.949569	0.607938	0.110273	0.215875	0.197184	0.086882	0.044458	0.603108	0.598592	0.128398	0.712781	0.071984	0.593583
38	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_3	Class 1	2285	1315	93	103	0.956868	0.933949	0.595391	0.113706	0.190782	0.160232	0.073297	0.040590	0.629083	0.580116	0.127633	0.731588	0.072638	0.525510
39	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Mean Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_4	Class 1	2185	1395	92	124	0.946297	0.938130	0.605689	0.128707	0.211378	0.184409	0.087195	0.048096	0.608272	0.592205	0.142939	0.711794	0.081633	0.574074
40	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_0	Class 1	2141	1419	104	133	0.941513	0.931714	0.601548	0.112933	0.203096	0.162586	0.080007	0.045294	0.598894	0.581293	0.148686	0.700879	0.085696	0.561181
41	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_1	Class 1	2241	1392	72	92	0.960566	0.950820	0.632310	0.104149	0.264620	0.207762	0.074086	0.036719	0.614432	0.588911	0.111650	0.726049	0.061995	0.560976
42	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_2	Class 1	2198	1412	78	109	0.952752	0.947651	0.612385	0.129117	0.224771	0.191752	0.084677	0.043758	0.607585	0.595876	0.127635	0.716361	0.071663	0.582888
43	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_3	Class 1	2343	1257	94	102	0.958282	0.930422	0.610182	0.119809	0.220364	0.171241	0.079041	0.045001	0.644099	0.585621	0.131190	0.742909	0.075055	0.520408
44	k-Nearest Neighbors (k-NN)	{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N...	Median Imputation	Undersampling	k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_4	Class 1	2142	1438	84	132	0.941953	0.944809	0.614230	0.115921	0.228461	0.209435	0.098516	0.053087	0.599052	0.604718	0.147816	0.704283	0.084076	0.611111

	Algorithm	Hyperparameters	Imputation Technique	Imbalance Class Technique	Model Unique Code	Execution Time	Init_Num_Class_0	Init_Num_Class_1	Res_Num_Class_0	Res_Num_Class_1	execution_time_seg	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
0	Logistic Regression	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '...	Zero Imputation	None	Logistic Regression_None_Zero Imputation_None_fold_0	0.24	14423	763	14423	763	0.24	Class 1	3556	4	236	1	0.999719	0.016667	0.599933	0.083530	0.199866	0.206333	0.020652	0.005700	0.936792	0.501548	0.008264	0.907492	0.200000	0.004219
1	Logistic Regression	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '...	Zero Imputation	None	Logistic Regression_None_Zero Imputation_None_fold_1	0.31	14350	836	14350	836	0.31	Class 1	3628	5	163	1	0.999724	0.029762	0.563974	0.050731	0.127948	0.174778	0.024164	0.008743	0.955755	0.502361	0.011765	0.935664	0.166667	0.006098
2	Logistic Regression	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '...	Zero Imputation	None	Logistic Regression_None_Zero Imputation_None_fold_2	0.25	14373	813	14373	813	0.25	Class 1	3605	5	187	0	1.000000	0.026042	0.552269	0.053489	0.104537	0.165550	-0.008265	-0.002572	0.949434	0.499307	0.000000	0.926089	0.000000	0.000000
3	Logistic Regression	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '...	Zero Imputation	None	Logistic Regression_None_Zero Imputation_None_fold_3	0.24	14383	804	14383	804	0.24	Class 1	3594	6	196	0	1.000000	0.029703	0.549608	0.060222	0.099216	0.173520	-0.009284	-0.003077	0.946786	0.499167	0.000000	0.922444	0.000000	0.000000
4	Logistic Regression	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '...	Zero Imputation	None	Logistic Regression_None_Zero Imputation_None_fold_4	0.24	14403	784	14403	784	0.24	Class 1	3578	2	214	2	0.999441	0.009259	0.550503	0.070592	0.101006	0.157107	0.062123	0.016146	0.943098	0.504350	0.018182	0.916500	0.500000	0.009259
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
715	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio...	Median Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_0	0.73	14423	763	763	763	0.73	Class 1	2231	1329	84	153	0.935822	0.940552	0.689108	0.120529	0.378217	0.296507	0.135013	0.078875	0.627864	0.636128	0.178010	0.723195	0.103239	0.645570
716	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio...	Median Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_1	0.73	14350	836	836	836	0.73	Class 1	2318	1315	61	103	0.957456	0.955669	0.687482	0.086227	0.374964	0.292018	0.111827	0.057216	0.637609	0.633044	0.130215	0.743442	0.072638	0.628049
717	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio...	Median Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_2	0.75	14373	813	813	813	0.75	Class 1	2293	1317	69	118	0.951058	0.950216	0.666724	0.086319	0.333448	0.269436	0.118798	0.063927	0.634975	0.633098	0.145499	0.737263	0.082230	0.631016
718	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio...	Median Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_3	0.77	14383	804	804	804	0.77	Class 1	2245	1355	69	127	0.946459	0.951545	0.681509	0.096901	0.363017	0.293169	0.123184	0.066204	0.624868	0.635785	0.151371	0.727830	0.085695	0.647959
719	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio...	Median Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_4	0.76	14403	784	784	784	0.76	Class 1	2326	1254	74	142	0.942464	0.944277	0.711052	0.130003	0.422105	0.325528	0.147550	0.086110	0.650158	0.653564	0.176179	0.743686	0.101719	0.657407

	Algorithm	Hyperparameters	Imputation Technique	Imbalance Class Technique	Model Unique Code	Execution Time	Init_Num_Class_0	Init_Num_Class_1	Res_Num_Class_0	Res_Num_Class_1	execution_time_seg	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
559	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	Median Imputation	None	LightGBM_None_Median Imputation_None_fold_4	0.97	14403	784	14403	784	0.97	Class 1	3579	1	215	1	0.999721	0.004630	0.723510	0.136000	0.447020	0.353011	0.043916	0.008139	0.943098	0.502175	0.009174	0.915995	0.500000	0.004630
579	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	Median Imputation	Oversampling	LightGBM_Oversampling_Median Imputation_None_fold_4	0.97	14403	784	14403	14403	0.97	Class 1	3075	505	133	83	0.973718	0.791536	0.707663	0.137440	0.415326	0.323081	0.155712	0.134426	0.831928	0.621599	0.206468	0.866205	0.141156	0.384259
578	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	Median Imputation	Oversampling	LightGBM_Oversampling_Median Imputation_None_fold_3	0.94	14383	804	14383	14383	0.94	Class 1	3015	585	122	74	0.976044	0.827440	0.690359	0.103601	0.380717	0.298929	0.125638	0.101592	0.813751	0.607526	0.173099	0.857780	0.112291	0.377551
577	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	Median Imputation	Oversampling	LightGBM_Oversampling_Median Imputation_None_fold_2	0.94	14373	813	14373	14373	0.94	Class 1	2990	620	107	80	0.973941	0.852820	0.694404	0.102240	0.388807	0.292502	0.142890	0.111302	0.808533	0.628031	0.180383	0.856578	0.114286	0.427807
576	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	Median Imputation	Oversampling	LightGBM_Oversampling_Median Imputation_None_fold_1	0.94	14350	836	14350	14350	0.94	Class 1	3073	560	105	59	0.981162	0.842105	0.688934	0.090925	0.377868	0.319675	0.113158	0.088452	0.824862	0.602807	0.150702	0.869898	0.095315	0.359756
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
483	XGBoost	{'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam...	None	None	XGBoost_None_None_None_fold_3	0.00	14383	804	14383	804	0.00	Class 1	3594	6	193	3	0.999166	0.030151	0.685021	0.120106	0.370041	0.304195	0.062059	0.024847	0.947576	0.506820	0.029268	0.924330	0.333333	0.015306
484	XGBoost	{'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam...	None	None	XGBoost_None_None_None_fold_4	0.00	14403	784	14403	784	0.00	Class 1	3577	3	214	2	0.999441	0.013825	0.712983	0.129065	0.425966	0.352933	0.053788	0.015565	0.942835	0.504211	0.018100	0.916363	0.400000	0.009259
540	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	None	None	LightGBM_None_None_None_fold_0	0.00	14423	763	14423	763	0.00	Class 1	3557	3	235	2	0.999438	0.012605	0.689955	0.132990	0.379909	0.292415	0.050672	0.013986	0.937319	0.503798	0.016529	0.908262	0.400000	0.008439
542	LightGBM	{'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl...	None	None	LightGBM_None_None_None_fold_2	0.00	14373	813	14373	813	0.00	Class 1	3604	6	186	1	0.999723	0.031250	0.703826	0.112467	0.407651	0.314831	0.018591	0.006779	0.949434	0.501843	0.010309	0.926590	0.142857	0.005348
481	XGBoost	{'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam...	None	None	XGBoost_None_None_None_fold_1	0.00	14350	836	14350	836	0.00	Class 1	3626	7	163	1	0.999724	0.041176	0.683174	0.091616	0.366347	0.300800	0.018491	0.007641	0.955228	0.502085	0.011628	0.935395	0.125000	0.006098

	Algorithm	Hyperparameters	Imputation Technique	Imbalance Class Technique	Model Unique Code	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
0	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Oversampling	Neural Networks_Oversampling_Zero Imputation_fold_0	Class 1	2721	839	136	101	0.964210	0.860513	0.618631	0.111923	0.237261	0.219866	0.106769	0.079889	0.743218	0.595243	0.171623	0.805838	0.107447	0.426160
1	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Oversampling	Neural Networks_Oversampling_Zero Imputation_fold_1	Class 1	2847	786	97	67	0.977008	0.890147	0.653543	0.083387	0.307085	0.265538	0.093613	0.063939	0.767448	0.596093	0.131760	0.834042	0.078546	0.408537
2	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Oversampling	Neural Networks_Oversampling_Zero Imputation_fold_2	Class 1	2911	699	105	82	0.972603	0.869403	0.646090	0.094143	0.292180	0.255255	0.131092	0.097716	0.788254	0.622437	0.169421	0.843730	0.104994	0.438503
3	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Oversampling	Neural Networks_Oversampling_Zero Imputation_fold_3	Class 1	2735	865	100	96	0.966090	0.896373	0.641382	0.101850	0.282764	0.266633	0.126982	0.087695	0.745785	0.624759	0.165946	0.814717	0.099896	0.489796
4	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Oversampling	Neural Networks_Oversampling_Zero Imputation_fold_4	Class 1	2812	768	125	91	0.968653	0.860022	0.650405	0.108391	0.300811	0.272589	0.114475	0.086204	0.764752	0.603386	0.169302	0.823502	0.105937	0.421296
5	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Undersampling	Neural Networks_Undersampling_Zero Imputation_fold_0	Class 1	2315	1245	110	127	0.947993	0.918819	0.605889	0.174622	0.211778	0.228541	0.093739	0.057539	0.643139	0.593073	0.157862	0.735168	0.092566	0.535865
6	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Undersampling	Neural Networks_Undersampling_Zero Imputation_fold_1	Class 1	2164	1469	62	102	0.954987	0.959504	0.588771	0.109206	0.177543	0.221773	0.089819	0.042701	0.596787	0.608801	0.117579	0.711865	0.064927	0.621951
7	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Undersampling	Neural Networks_Undersampling_Zero Imputation_fold_2	Class 1	2475	1135	124	63	0.975177	0.901509	0.596698	0.142273	0.193396	0.229597	0.010474	0.006313	0.668422	0.511247	0.090975	0.762447	0.052588	0.336898
8	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Undersampling	Neural Networks_Undersampling_Zero Imputation_fold_3	Class 1	2531	1069	114	82	0.968618	0.903635	0.610656	0.163082	0.221312	0.227211	0.058456	0.036755	0.688356	0.560711	0.121752	0.775003	0.071242	0.418367
9	Neural Networks	{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e...	Zero Imputation	Undersampling	Neural Networks_Undersampling_Zero Imputation_fold_4	Class 1	1747	1833	71	145	0.923362	0.962710	0.560347	0.177950	0.120695	0.195143	0.073864	0.032960	0.498419	0.579643	0.132179	0.617967	0.073306	0.671296

	Algorithm	Hyperparameters	Imputation Technique	Imbalance Class Technique	Model Unique Code	Execution Time	Init_Num_Class_0	Init_Num_Class_1	Res_Num_Class_0	Res_Num_Class_1	execution_time_seg	Class	TP	TN	FP	FN	Sensitivity_TPR	Specificity	AUC_ROC	AUC_PRC	Gini	KS	MCC	Kappa	Accuracy	Balanced_Accuracy	F1	F1_Weighted	Precision	Recall
0	Random Forest	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No...	Zero Imputation	None	Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_0	15.16	16208	876	16208	876	15.16	Class 1	1770	5	122	2	0.998871	0.039370	0.604995	0.112620	0.209991	0.193180	0.054269	0.023722	0.933123	0.506656	0.030534	0.904325	0.285714	0.016129
1	Random Forest	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No...	Zero Imputation	None	Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_1	16.00	16197	887	16197	887	16.00	Class 1	1776	10	110	3	0.998314	0.083333	0.665332	0.119504	0.330664	0.290881	0.060105	0.035780	0.936809	0.510475	0.047619	0.912593	0.230769	0.026549
2	Random Forest	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No...	Zero Imputation	None	Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_2	15.78	16165	919	16165	919	15.78	Class 1	1804	14	78	3	0.998340	0.152174	0.694706	0.089273	0.389412	0.342745	0.062938	0.047124	0.951553	0.514668	0.061224	0.936153	0.176471	0.037037
3	Random Forest	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No...	Zero Imputation	None	Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_3	16.04	16168	917	16168	917	16.04	Class 1	1806	9	83	0	1.000000	0.097826	0.636679	0.080354	0.273358	0.258827	-0.014761	-0.008630	0.951528	0.497521	0.000000	0.932518	0.000000	0.000000
4	Random Forest	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No...	Zero Imputation	None	Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_4	15.51	16181	904	16181	904	15.51	Class 1	1794	8	96	0	1.000000	0.076923	0.657643	0.116523	0.315286	0.282036	-0.015017	-0.007842	0.945205	0.497780	0.000000	0.922676	0.000000	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
955	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'...	Zero Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_...	6.67	16176	909	909	909	6.67	Class 1	1137	670	33	58	0.951464	0.953058	0.688300	0.096823	0.376600	0.316151	0.117131	0.061655	0.629610	0.633291	0.141636	0.734024	0.079670	0.637363
956	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'...	Zero Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_...	6.63	16181	904	904	904	6.63	Class 1	1137	665	38	58	0.951464	0.945946	0.676846	0.105696	0.353693	0.311217	0.106105	0.057466	0.629610	0.617566	0.141636	0.732385	0.080221	0.604167
957	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'...	Zero Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_...	6.63	16185	900	900	900	6.63	Class 1	1096	702	39	61	0.947277	0.947368	0.663968	0.086106	0.327937	0.258943	0.100046	0.053156	0.609589	0.609783	0.141367	0.715430	0.079948	0.610000
958	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'...	Zero Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_...	6.73	16206	879	879	879	6.73	Class 1	1106	671	35	86	0.927852	0.950425	0.702368	0.127047	0.404735	0.363981	0.166216	0.096587	0.628030	0.666571	0.195900	0.722215	0.113606	0.710744
959	HistGradientBoostingClassifier	{'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'...	Zero Imputation	Undersampling	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_...	8.73	16180	905	905	905	8.73	Class 1	1158	645	28	67	0.945306	0.958395	0.737484	0.122450	0.474969	0.396771	0.156518	0.085256	0.645416	0.673763	0.166047	0.744369	0.094101	0.705263

	Model Code	AUC_ROC
549	LightGBM_None_None_MinMaxScaler [1, 2]	0.770786
859	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler	0.767262
739	HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [0, 1]	0.759383
949	HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.756073
902	HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]	0.755134
909	HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]	0.754663
559	LightGBM_None_Zero Imputation_MinMaxScaler [1, 2]	0.753884
469	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.752801
789	HistGradientBoostingClassifier_None_None_MinMaxScaler [1, 2]	0.752763
319	XGBoost_None_Zero Imputation_MinMaxScaler [1, 2]	0.750398
799	HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [1, 2]	0.749062
849	HistGradientBoostingClassifier_None_None_StandardScaler	0.747999
81	Random Forest_Undersampling_Zero Imputation_StandardScaler	0.747470
579	LightGBM_Oversampling_Zero Imputation_MinMaxScaler [1, 2]	0.747290
462	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.746740
671	LightGBM_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.746294
491	LightGBM_None_Zero Imputation_MinMaxScaler [0, 1]	0.746294
819	HistGradientBoostingClassifier_Oversampling_Zero Imputation_MinMaxScaler [1, 2]	0.745956
129	Gradient Boosting Machines (GBM)_None_Zero Imputation_MinMaxScaler [0, 1]	0.745632
139	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]	0.745480
371	XGBoost_None_Zero Imputation_StandardScaler	0.745429
181	Gradient Boosting Machines (GBM)_None_Zero Imputation_StandardScaler	0.745380
199	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_StandardScaler	0.745159
912	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.744482
239	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.744216
259	XGBoost_None_Zero Imputation_MinMaxScaler [0, 1]	0.744178
439	XGBoost_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.744178
189	Gradient Boosting Machines (GBM)_None_Zero Imputation_StandardScaler	0.743752
918	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.743065
161	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]	0.742927

	Model Code	KS
359	XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2]	0.441212
549	LightGBM_None_None_MinMaxScaler [1, 2]	0.437411
762	HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]	0.436010
918	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.430771
559	LightGBM_None_Zero Imputation_MinMaxScaler [1, 2]	0.430131
902	HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]	0.427271
462	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.425804
81	Random Forest_Undersampling_Zero Imputation_StandardScaler	0.421523
859	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler	0.421269
949	HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.419003
319	XGBoost_None_Zero Imputation_MinMaxScaler [1, 2]	0.417410
82	Random Forest_Undersampling_Zero Imputation_StandardScaler	0.417003
891	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler	0.416127
469	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.412430
799	HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [1, 2]	0.411262
12	Random Forest_Oversampling_Zero Imputation_MinMaxScaler [0, 1]	0.410158
111	Random Forest_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.409314
912	HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.408996
201	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler	0.408784
191	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_StandardScaler	0.408660
161	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]	0.408457
941	HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.407853
92	Random Forest_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.406980
371	XGBoost_None_Zero Imputation_StandardScaler	0.406559
698	LightGBM_Oversampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.406377
538	LightGBM_Undersampling_Zero Imputation_MinMaxScaler [0, 1]	0.406363
131	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]	0.405752
598	LightGBM_Undersampling_Zero Imputation_MinMaxScaler [1, 2]	0.405559
102	Random Forest_Oversampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.405146
739	HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [0, 1]	0.404711

	Model Code	Balanced_Accuracy
81	Random Forest_Undersampling_Zero Imputation_StandardScaler	0.704494
111	Random Forest_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.703203
762	HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]	0.699303
469	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.697606
462	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.697500
598	LightGBM_Undersampling_Zero Imputation_MinMaxScaler [1, 2]	0.696131
359	XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2]	0.695648
949	HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.694821
761	HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]	0.691281
419	XGBoost_Undersampling_Zero Imputation_StandardScaler	0.690951
538	LightGBM_Undersampling_Zero Imputation_MinMaxScaler [0, 1]	0.688148
711	LightGBM_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.687471
208	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler	0.686862
142	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_MinMaxScaler [0, 1]	0.686071
941	HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.686071
82	Random Forest_Undersampling_Zero Imputation_StandardScaler	0.686010
864	HistGradientBoostingClassifier_Oversampling_None_StandardScaler	0.685650
201	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler	0.685457
891	HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler	0.683722
461	XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]	0.683381
169	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]	0.683227
585	LightGBM_Undersampling_None_MinMaxScaler [1, 2]	0.683058
139	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]	0.682395
479	XGBoost_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]	0.682071
405	Random Forest_Undersampling_Zero Imputation_None	0.681244
889	HistGradientBoostingClassifier_Undersampling_None_StandardScaler	0.680702
138	Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]	0.680132
202	Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler	0.679501
772	HistGradientBoostingClassifier_Undersampling_Zero Imputation_MinMaxScaler [0, 1]	0.679410
641	LightGBM_Undersampling_None_StandardScaler	0.678956

	number	value	datetime_start	datetime_complete	duration	params_learning_rate	params_max_depth	params_n_estimators	state
0	0	0.729730	2024-01-07 20:02:46.799686	2024-01-07 20:02:47.861923	0 days 00:00:01.062237	0.000315	5	535	COMPLETE
1	1	0.729730	2024-01-07 20:02:47.863924	2024-01-07 20:02:49.378263	0 days 00:00:01.514339	0.000198	70	516	COMPLETE
2	2	0.756757	2024-01-07 20:02:49.379263	2024-01-07 20:02:50.126430	0 days 00:00:00.747167	0.006110	125	243	COMPLETE
3	3	0.729730	2024-01-07 20:02:50.127430	2024-01-07 20:02:51.185667	0 days 00:00:01.058237	0.014404	115	901	COMPLETE
4	4	0.540541	2024-01-07 20:02:51.186667	2024-01-07 20:02:52.376935	0 days 00:00:01.190268	0.044723	46	926	COMPLETE
5	5	0.756757	2024-01-07 20:02:52.377935	2024-01-07 20:02:53.842262	0 days 00:00:01.464327	0.002101	73	681	COMPLETE
6	6	0.756757	2024-01-07 20:02:53.844263	2024-01-07 20:02:54.989519	0 days 00:00:01.145256	0.014741	81	688	COMPLETE
7	7	0.648649	2024-01-07 20:02:54.990520	2024-01-07 20:02:55.486631	0 days 00:00:00.496111	0.002174	6	232	COMPLETE
8	8	0.621622	2024-01-07 20:02:55.487631	2024-01-07 20:02:55.808703	0 days 00:00:00.321072	0.069402	69	302	COMPLETE
9	9	0.729730	2024-01-07 20:02:55.809703	2024-01-07 20:02:56.549870	0 days 00:00:00.740167	0.000399	204	367	COMPLETE
10	10	0.702703	2024-01-07 20:02:56.550870	2024-01-07 20:02:56.854937	0 days 00:00:00.304067	0.008422	327	125	COMPLETE
11	11	0.729730	2024-01-07 20:02:56.856938	2024-01-07 20:02:58.489303	0 days 00:00:01.632365	0.001687	172	758	COMPLETE
12	12	0.729730	2024-01-07 20:02:58.491304	2024-01-07 20:03:00.353721	0 days 00:00:01.862417	0.000953	166	684	COMPLETE
13	13	0.729730	2024-01-07 20:03:00.354721	2024-01-07 20:03:01.447966	0 days 00:00:01.093245	0.004099	242	376	COMPLETE
14	14	0.756757	2024-01-07 20:03:01.448966	2024-01-07 20:03:02.339166	0 days 00:00:00.890200	0.005234	134	491	COMPLETE
15	15	0.729730	2024-01-07 20:03:02.341166	2024-01-07 20:03:02.603226	0 days 00:00:00.262060	0.000837	268	106	COMPLETE
16	16	0.567568	2024-01-07 20:03:02.604227	2024-01-07 20:03:03.396402	0 days 00:00:00.792175	0.031024	117	799	COMPLETE
17	17	0.702703	2024-01-07 20:03:03.397402	2024-01-07 20:03:04.939748	0 days 00:00:01.542346	0.001111	388	629	COMPLETE
18	18	0.702703	2024-01-07 20:03:04.940749	2024-01-07 20:03:05.972980	0 days 00:00:01.032231	0.000120	212	406	COMPLETE
19	19	0.729730	2024-01-07 20:03:05.974981	2024-01-07 20:03:06.413078	0 days 00:00:00.438097	0.007565	169	206	COMPLETE